Context Window Management Techniques for AI Agents

Short answer: Context window management techniques come down to five controls before every model call: compress the input, split anything too long into segments, truncate pipeline context at a fixed cap, dedup overlapping passages, and enforce a monthly quota. SmartGate exposes all five as MCP tools — the default compression ratio is 0.5, and the Free plan meters 2M tokens a month at 120 MCP requests per minute per key.

Key takeaways

• Compression is a ratio, and the caller wins. smart_context_gate defaults to 0.5, but an explicit ratio in the request overrides the team default; the precedence lives in resolve_compress_ratio.
• Split before you compress. splitForPlaygroundCompress caps total input, hard-splits oversized blocks, and reports partial coverage instead of silently dropping a tail.
• Truncate only where a cap exists. _truncate_pipeline_text returns text untouched when no per-template cap is set, and otherwise cuts at exactly the cap.
• Dedup is a threshold, not a delete. smart_dedup ships at a 0.9 threshold, and find_representative reranks survivors for diversity.
• Bound the candidate set or you pay for it. compute_candidate_limit derives the rerank budget — 10% of the corpus, floor 100, ceiling 1,000.
• Memory and quotas are both team-scoped. smart_memory moves history out of the prompt with add/search/get/delete, and checkQuota returns one exceeded decision per month.
• Do this before your first loop: set the compression ratio and the monthly cap, then connect one MCP client and start free.

---

The short version for whoever signs the invoice

A context window is a budget, and an agent spends it every turn whether or not it learns anything. The expensive habits are boring ones: re-sending the same fetched page, pasting a whole issue thread to answer one question, keeping full history because trimming it feels unsafe. That is plumbing, not a model problem, and it is fixable before the model is called. The loop that makes it expensive in the first place is the object What Is an Agentic Workflow defines.

SmartGate is an MCP-native algorithm gateway for token control, traffic shaping, and agent audit — not a model host. You keep your agent and its host model, and connect the gateway as one more MCP server at the stateless endpoint (https://smartgate.network/api/mcp, Streamable HTTP POST). What changes is that the tool side of the loop — fetch, search, compress, dedup, budget, memory, pipelines — is measured.

Free gives 2M tokens/month, all seven tools (smart_fetch, smart_search, smart_context_gate, smart_dedup, smart_budget_guard, smart_memory, smart_pipe), 120 MCP requests/min per key, and 7-day logs, no card. Pro starts at $18/month ($5 first month), 300 req/min/key, 30-day logs, capped near $36/month; Teams starts at $55/month with 600 req/min/key and 90-day logs; Enterprise is contract-based at 1,200 req/min/key. The billing line is "Pay for the platform. Share only when you save." — the share begins only after $15 of measured savings (pricing).

What the research says about long context

There is a decent pile of published work on long context, and it points the same way these twelve code paths do. Anthropic's context-engineering write-up frames the job as curating the smallest set of high-signal tokens rather than filling the window (Anthropic). Chroma's Context Rot study reports reliability degrading as input length grows, even for an unchanged task (Chroma Research). Lost in the Middle showed that information buried mid-input is used less reliably than the same information at the edges (arXiv:2307.03172), LLMLingua showed prompt compression preserving task performance while cutting tokens (arXiv:2310.05736), and MemGPT is the canonical argument for moving history into an external memory tier (arXiv:2310.08560).

Two conclusions follow, both operational. Extra tokens dilute attention and raise cost at the same time, so more context is not more understanding; and the fix is mechanical — compress, segment, truncate, dedup, offload — which is why it belongs in a gateway every tool call passes through rather than in each agent's prompt template. MCP defines tools and resources a client discovers per session (MCP specification), so a gateway can sit in that path without any agent being rewritten. How that gateway fits a wider platform, and which controls belong in it rather than in each client, is covered in Enterprise AI Gateway Architecture Best Practices.

smart_context_gate: compress the context window before the model call

Compression is the cheapest lever in the stack. The handler takes the long text, a target ratio, and an optional purpose that pre-filters paragraphs toward the current goal, then dispatches through the gateway's audit wrapper:

# backend/smartgate/api/mcp.py — source lines 150–174 (smart_context_gate)
@server.tool(
        name="smart_context_gate",
        description=TOOL_DESCRIPTIONS["smart_context_gate"],
        annotations=tool_annotations("smart_context_gate"),
    )
    async def smart_context_gate(
        text: str = Field(description="Long text to compress before the host LLM call."),
        ratio: float = Field(
            default=0.5,
            description="Target compression ratio (e.g. 0.3–0.7).",
        ),
        purpose: str | None = Field(
            default=None,
            description="Optional goal to pre-filter paragraphs (step intent, user query).",
        ),
    ) -> str:
        _, registry = _app_state()
        module = registry.get("context_gate")
        ctx = _tool_ctx()
        return await _run_with_audit(
            "compress",
            ctx,
            module.process(ctx, text=text, ratio=ratio, purpose=purpose),
            {"ratio": ratio, "purpose": purpose},
        )

Three details matter. The ratio defaults to 0.5 — a target, not a promise, with 0.3–0.7 as the range where compression stops being free. The purpose argument makes compression goal-aware, keeping paragraphs that serve the current question instead of the first half of the document. And the call is audited as compress with the ratio recorded, so a thin answer can be traced back to a number.

resolve_compress_ratio: who decides the effective compression ratio

One setting, three sources, and an explicit order of precedence:

# backend/smartgate/core/effective_settings.py — source lines 69–74 (resolve_compress_ratio)
def resolve_compress_ratio(request_state: Any, body_ratio: float | None) -> float:
    if body_ratio is not None:
        return body_ratio
    es = getattr(request_state, "effective_settings", None) or {}
    defaults = es.get("defaults") or {}
    return float(defaults.get("compress_ratio", 0.5))

A ratio supplied in the request body wins, because the caller knows what this specific call is for. Otherwise the team's effective settings supply the default (compress_ratio, again 0.5), so a team tunes compression once rather than in every prompt. What the function avoids is the pattern that makes tuning impossible: a hardcoded ratio buried in a call chain. If compression output looks wrong, this is the function that says which of the three sources to change.

splitForPlaygroundCompress: split long input before compression

You cannot compress a large page as one blob, and the failure mode of trying is a silent tail truncation. So this function splits first: it caps the input at a maximum total size and sets a partialCoverage flag when anything was dropped, splits the capped text into blocks, and hard-splits any block still larger than the segment limit:

# lib/smartgate/playground-content.ts — source lines 113–166 (splitForPlaygroundCompress)
function splitForPlaygroundCompress(text: string): PlaygroundSplitResult {
  const totalChars = text.length;
  const capped = text.slice(0, PLAYGROUND_MAX_TOTAL_CHARS);
  const partialCoverage = totalChars > PLAYGROUND_MAX_TOTAL_CHARS;

  let blocks = splitIntoBlocks(capped);
  let hardSplit = false;

  const expanded: string[] = [];
  for (const block of blocks) {
    if (block.length > PLAYGROUND_SEGMENT_CHARS) {
      hardSplit = true;
      expanded.push(...hardSplitBlock(block, PLAYGROUND_SEGMENT_CHARS));
    } else {
      expanded.push(block);
    }
  }
  blocks = expanded;

  const segments: string[] = [];
  let buf = "";

  const flushSegment = () => {
    if (buf.trim()) {
      segments.push(buf.trimEnd());
      buf = "";
    }
  };

  for (const block of blocks) {
    const candidate = buf ? `${buf}\n\n${block}` : block;
    if (candidate.length <= PLAYGROUND_SEGMENT_CHARS) {
      buf = candidate;
    } else {
      flushSegment();
      buf = block;
    }
  }
  flushSegment();

  if (segments.length === 0 && capped.trim()) {
    segments.push(capped.trim());
  }

  const processedChars = segments.reduce((sum, s) => sum + s.length, 0);

  return {
    segments,
    processedChars,
    totalChars,
    hardSplit,
    partialCoverage,
  };
}

The packing loop accumulates blocks in a buffer until the next block would overflow the segment, then flushes and starts again. The return value is the caller's receipt — segments to compress, processedChars against totalChars, plus hardSplit and partialCoverage as two honesty flags.

flushSegment: when the compressor flushes a segment

Every packing loop needs one boundary rule, and this is it:

# lib/smartgate/playground-content.ts — source lines 135–140 (flushSegment)
const flushSegment = () => {
    if (buf.trim()) {
      segments.push(buf.trimEnd());
      buf = "";
    }
  };

A segment is emitted only when the buffer holds non-whitespace, trailing whitespace is trimmed before the push, and the buffer resets so the next segment starts clean. Two properties matter downstream: nothing empty is ever emitted, so no compressor call is spent on whitespace, and trimming happens at the boundary rather than per block, so a segment ending mid-sentence keeps its internal newlines. The helper closes over the surrounding buffer, which is why it lives inside the function it serves.

_truncate_pipeline_text: truncate pipeline text when the budget is tight

Truncation is the most primitive control, and this is the honest version of it: per-template caps, no cap means no cut, and a cut means a cut rather than a summary.

# backend/smartgate/core/pipeline.py — source lines 176–180 (_truncate_pipeline_text)
def _truncate_pipeline_text(text: str, pipeline_template: str) -> str:
    cap = PIPELINE_CONTEXT_GATE_MAX_CHARS.get(pipeline_template or "")
    if not cap or len(text) <= cap:
        return text
    return text[:cap]

The lookup is keyed by pipeline template, so research and read runs can carry different ceilings, and an unconfigured template passes text through instead of inventing a default. When a cap does apply, the result is text[:cap] — the beginning of the text and nothing else. That bluntness is the point: deterministic, free, and the last line of defence before a pipeline stage hands an oversized blob to a model.

smart_dedup: semantic dedup for overlapping context passages

Agents rarely overflow a window with one huge document. They overflow it by assembling the same document repeatedly: the same changelog from three searches, the same paragraph from two fetches, a summary and its source.

# backend/smartgate/api/mcp.py — source lines 176–196 (smart_dedup)
@server.tool(
        name="smart_dedup",
        description=TOOL_DESCRIPTIONS["smart_dedup"],
        annotations=tool_annotations("smart_dedup"),
    )
    async def smart_dedup(
        texts: list[str] = Field(description="List of text passages to deduplicate."),
        threshold: float = Field(
            default=0.9,
            description="Similarity threshold (0.0–1.0); higher keeps fewer duplicates.",
        ),
    ) -> str:
        _, registry = _app_state()
        module = registry.get("dedup")
        ctx = _tool_ctx()
        return await _run_with_audit(
            "dedup",
            ctx,
            module.process(ctx, texts=texts, threshold=threshold),
            {"threshold": threshold},
        )

The tool takes a list of passages and a similarity threshold that defaults to 0.9, and its own description states the trade-off: higher keeps fewer duplicates. That phrasing matters, because 0.9 means near-identical rather than merely related — lowering it starts discarding passages that are only topically adjacent. The call is audited as dedup with the threshold recorded, which is the field you want when two similar paragraphs both survived.

Dedup at this layer is what keeps an advanced RAG architecture affordable: a four-stage retrieval stack pays for every passage it carries forward, and the near-duplicates are the cheapest ones to drop.

find_representative: the representative passage in a duplicate cluster

Removing exact duplicates is the easy half. The hard half is what to keep when ten passages cover the same ground, because centrality and redundancy look alike:

# backend/smartgate/modules/dedup/algorithm.py — source lines 326–352 (find_representative)
def find_representative(
        self,
        records: Sequence[Record],
        selection_size: int = 10,
        candidate_limit: int | Literal["auto"] = "auto",
        diversity: float = 0.5,
        strategy: Strategy | str = Strategy.MMR,
    ) -> FilterResult:
        """
        Find representative samples from a given set of records against the fitted index.

        First, the records are ranked using average similarity.
        Then, the top candidates are re-ranked using Maximal Marginal Relevance (MMR)
        to select a diverse set of representatives.

        :param records: The records to rank and select representatives from.
        :param selection_size: Number of representatives to select.
        :param candidate_limit: Number of top candidates to consider for diversity reranking.
            Defaults to "auto", which calculates the limit based on the total number of records.
        :param diversity: Trade-off between diversity (1.0) and relevance (0.0). Default is 0.5.
        :param strategy: Diversification strategy (MMR, MSD, DPP, COVER, SSD). Default is MMR.
        :return: A FilterResult with the diversified candidates.
        """
        ranking = self._rank_by_average_similarity(records)
        if candidate_limit == "auto":
            candidate_limit = compute_candidate_limit(total=len(ranking.selected), selection_size=selection_size)
        return self._diversify(ranking, candidate_limit, selection_size, diversity, strategy)

Records are first ranked by average similarity to the fitted index, producing a candidate pool; the pool is then re-ranked with Maximal Marginal Relevance to select passages that are relevant and mutually diverse. diversity sits at 0.5 by default — the midpoint between relevance and diversity — with selection_size at 10 representatives. The strategy is swappable (MMR, MSD, DPP, COVER, SSD), and candidate_limit: auto defers to the next function rather than guessing a pool size inline.

compute_candidate_limit: how many dedup candidates to consider

Diversity reranking is expensive work, so the candidate pool is a cost decision:

# backend/smartgate/modules/dedup/utils.py — source lines 88–113 (compute_candidate_limit)
def compute_candidate_limit(
    total: int,
    selection_size: int,
    fraction: float = 0.1,
    min_candidates: int = 100,
    max_candidates: int = 1000,
) -> int:
    """
    Compute the 'auto' candidate limit based on the total number of records.

    :param total: Total number of records.
    :param selection_size: Number of representatives to select.
    :param fraction: Fraction of total records to consider as candidates.
    :param min_candidates: Minimum number of candidates.
    :param max_candidates: Maximum number of candidates.
    :return: Computed candidate limit.
    """
    # 1) fraction of total
    limit = int(total * fraction)
    # 2) ensure enough to pick selection_size
    limit = max(limit, selection_size)
    # 3) enforce lower bound
    limit = max(limit, min_candidates)
    # 4) enforce upper bound (and never exceed the dataset)
    limit = min(limit, max_candidates, total)
    return limit

Four rules apply in a fixed order: 10% of the corpus by default, raised so there are always at least as many candidates as representatives requested, raised again to a floor of 100, then clamped to a ceiling of 1,000 and to the dataset size. The order matters — the floor applies after the fraction, so small corpora still get a real pool, and the ceiling applies last, so a large corpus cannot turn one dedup call into an unbounded scan.

get_pure_token: count pure tokens inside the compressor

Token accounting inside a compressor has a nasty failure mode: subword markers counted as content. Word-piece tokenizers prefix continuation pieces and SentencePiece marks word starts, so a naive counter treats markers as text and ratios get measured against padded numbers. This function strips the marker for the two tokenizers the compressor supports, and refuses the rest:

# backend/smartgate/modules/context_gate/utils.py — source lines 105–111 (get_pure_token)
def get_pure_token(token, model_name):
    if "bert-base-multilingual-cased" in model_name:
        return token.lstrip("##")
    elif "xlm-roberta-large" in model_name:
        return token.lstrip("▁")
    else:
        raise NotImplementedError()

The raise NotImplementedError() branch is the design choice worth copying. An unsupported tokenizer fails loudly instead of returning a count that is mostly marker characters, which is what makes the ratio numbers elsewhere in this article trustworthy: either the count is right, or the call tells you it cannot count.

smart_memory: move agent history into team memory

The most effective way to shrink a context window is to stop putting things in it. Memory makes that possible: instead of carrying a conversation's history in every prompt, the agent writes facts into a team-scoped store and reads back only what the current step needs:

# backend/smartgate/api/mcp.py — source lines 280–300 (smart_memory)
    ) -> str:
        _, registry = _app_state()
        module = registry.get("memory")
        ctx = _tool_ctx()
        params = _non_empty(
            action=action,
            query=query or None,
            text=text or None,
            messages=messages,
            user_id=user_id or None,
            memory_id=memory_id or None,
            top_k=top_k,
            threshold=threshold,
            metadata=metadata,
        )
        return await _run_with_audit(
            f"memory_{action}",
            ctx,
            module.process(ctx, **params),
            {"action": action},
        )

action is one of add, search, get or delete; search takes a query, add takes text or a messages payload, and top_k caps results at 10 by default over a similarity threshold of 0.1. The _non_empty wrapper turns empty strings into absent arguments, so a caller that always passes user_id="" does not create a scoped-in-theory record, and each operation is audited as memory_<action>. Memory belongs to the team and an optional user_id, so two agents on one key share a store — split keys when that is not what you want. What to store, when retrieval is allowed to run and what each of those two paths costs is the design question on Agent Memory Architecture.

checkQuota: the monthly token quota check per team

Every control above saves tokens; this one bounds the month:

# lib/usage/index.ts — source lines 101–129 (checkQuota)
async function checkQuota(
  teamId: string,
  monthlyTokenLimit?: number | bigint | null,
  plan?: string | null
): Promise<UsageQuota> {
  const normalizedPlan = (plan ?? "FREE").toUpperCase();
  let limit =
    monthlyTokenLimit != null ? Number(monthlyTokenLimit) : undefined;
  if (limit == null || !Number.isFinite(limit) || limit <= 0) {
    limit = PLAN_DEFAULT_LIMITS[normalizedPlan] ?? PLAN_DEFAULT_LIMITS.FREE!;
  }

  const { monthly } = await getCurrentUsage(teamId);
  if (limit >= Number.MAX_SAFE_INTEGER / 2) {
    return {
      limit: Infinity,
      used: monthly,
      remaining: Infinity,
      exceeded: false,
    };
  }

  return {
    limit,
    used: monthly,
    remaining: Math.max(0, limit - monthly),
    exceeded: monthly >= limit,
  };
}

The precedence inside checkQuota is the whole story. The plan name is upper-cased with FREE as the default; an explicit monthly limit is used only when it is present, finite and positive; otherwise the plan's default limit applies, falling back to FREE. Current usage comes from the team's monthly counter. One branch treats any limit at or above half of the platform's maximum safe integer as infinity — an explicit no-cap rather than a number large enough to look like one, with exceeded hard-coded false. Otherwise the caller gets limit, used, remaining and a single exceeded boolean, which is what separates a quota report from a quota. What that cap is worth in money, and how the savings get measured, is the other half of token control, worked through in Token Optimization Techniques for AI Apps. The cap, the retention window behind it and the counters that catch a regression are the operating layer LLMOps maps out.

smart_pipe: a multi step pipeline so context is not refetched

The last technique removes repetition instead of shrinking it. Fetching, compressing and remembering as three separate tool calls means three round trips and usually three copies of the same text in the transcript:

# backend/smartgate/api/mcp.py — source lines 343–387 (smart_pipe)
        payload = await engine.run(
            ctx,
            template=template,
            steps=steps,
            inputs=run_inputs,
        )
        data = payload if isinstance(payload, dict) else {"result": payload}
        merged_data = {
            **data,
            "results": payload.get("results") or [],
        }
        steps_meta = payload.get("pipeline_steps") or []
        pipeline_ok = bool(steps_meta) and all(s.get("success") for s in steps_meta)
        failed_step = next((s for s in steps_meta if not s.get("success")), None)
        failed_result = next(
            (r for r in (payload.get("results") or []) if not r.get("success")),
            None,
        )
        step_error = None
        if failed_result:
            step_error = failed_result.get("error")
        elif failed_step:
            step_error = failed_step.get("error")
        if not pipeline_ok:
            merged_data["failed_step"] = failed_step.get("step") if failed_step else None
            merged_data["failed_tool"] = failed_step.get("tool") if failed_step else None
            merged_data["step_error"] = step_error
        tool_result = ToolResult(
            success=pipeline_ok,
            data=merged_data,
            error=None if pipeline_ok else (step_error or "pipeline step failed"),
            meta={},
        )
        audit_extra = {
            **params,
            "trace_kind": "pipeline",
            "pipeline_template": template or None,
            "pipeline_steps": payload.get("pipeline_steps") or [],
        }
        return await _run_with_audit(
            "pipe",
            ctx,
            _identity_result(tool_result),
            audit_extra,
        )

The excerpt is the return path, and it is where the design pays off. The engine runs the template or the explicit step list and reports per-step metadata; pipeline_ok is true only when there is at least one step and every step succeeded. On failure the response carries failed_step, failed_tool and step_error separately, so "the fetch broke" and "the query returned nothing" are distinguishable — the difference between retrying and rephrasing. The run is also one audit trace with trace_kind: pipeline, so a research workflow is one entry, not four calls.

The decisions that keep such a chain operable — which steps may be retried, what each one returns, and how a degraded step reports itself — are the subject of agent pipeline, which is the cluster's page on the chain itself rather than on the context it carries.

How SmartGate compares

The category splits by where the trimming happens and what it can enforce. SmartGate's scope is deliberately narrow: it governs tool traffic, and it charges a share only after it has saved you money.

	Where the trimming happens	What it can enforce	What you pay
SmartGate	Gateway, in the tool path: compression, segmenting, truncation, dedup, team memory, quotas	Hard monthly token cap per team, per-key MCP rate limits, per-call audit rows	Free: 2M tokens/mo, all seven tools, 120 req/min/key. Pro from $18/mo (first month $5), ~$36/mo cap, share only after $15 saved (pricing)
App-side trimming in your own code	Inside your agent: prompt templates, summary buffers, manual chunking	Whatever you write; nothing outside your process, nothing per team	Engineering time; no rate limit, cap, or audit trail shared across clients
Local stdio compression servers	On the developer's machine, in-process	Compression and filtering for one host; nothing cross-team	Free/open source; you own the runtime, the versions, and the blast radius
LLM routers and observability proxies	Model calls, prompt routing and token accounting	Model-side spend and telemetry	Usage-based on model traffic — a different line item from tool traffic

Two honest readings. If your problem is one developer wanting shorter prompts in one editor, a local compression server or a summary buffer in your own code is a reasonable answer: free, private, enough. The moment the question becomes which agent spent the team's tokens and what stops it next month, trimming in application code stops being sufficient — that is a quota and an audit trail, and those live in something every client shares. The savings share is what makes the incentive legible: a team that saves nothing pays only the platform fee. Where that gateway sits in a wider agent stack — registry, tool names, transport and provenance — is the architecture comparison on AI Agent Architecture.

How to get started

1. Connect one client. Mint a key and add the hosted, stateless MCP endpoint (https://smartgate.network/api/mcp, POST, Streamable HTTP) as a server in your MCP host, so the seven tools appear in the agent's tool list.
2. Turn on compression and dedup before autonomy. Run smart_fetch plus smart_context_gate over material you already pull by hand, add smart_dedup where your searches overlap, and watch the token count on the usage report.
3. Pick the ratio deliberately. Start from the 0.5 default, then set the team's compress_ratio to whatever survives your own question-answering checks.
4. Set the cap, then move history out of the prompt. Give the team a monthly token limit so checkQuota has something to enforce, push history into smart_memory, and fold multi-step research into smart_pipe so the same page is fetched once. Whether a process is worth running this way at all is the adoption question set out on AI Workflow Automation.

Start with the Free plan — 2M tokens/month, all seven tools, no card: start free, then compare caps, rate limits and log retention windows against your workload on the pricing page.

Limitations and what this does not do

• Compression is lossy and ratio-dependent. A 0.5 target is a starting point, not a validated setting for your corpus; test ratios against questions whose answers you already know.
• Truncation is blind. _truncate_pipeline_text returns the head of the text at a configured cap — it cannot tell a boilerplate footer from a crucial paragraph, and the tail is gone.
• Dedup and diversity are trade-offs you own. A 0.9 threshold lets lightly reworded duplicates through, and raising diversity can drop the single best passage from the representative set.
• Quotas are per team, not per agent. Agents sharing a key share the cap and the rate limit; split keys for independent budgets, and note that log retention (7/30/90/180 days) is a plan entitlement, not an archive.
• A gateway is not a sandbox or a summarizer. It cannot judge whether a fetched page should have been fetched, and it does not replace prompt design — compaction and clear instructions remain your responsibility.

Sources

• SmartGate — product site and pricing: https://smartgate.network · https://smartgate.network/pricing
• SmartGate — hosted MCP endpoint and tool reference: https://smartgate.network/docs
• Anthropic — Effective context engineering for AI agents: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
• Anthropic — Building effective agents: https://www.anthropic.com/engineering/building-effective-agents
• Chroma Research — Context Rot (reliability degrades as input grows): https://research.trychroma.com/context-rot
• Lost in the Middle: How Language Models Use Long Contexts (arXiv:2307.03172): https://arxiv.org/abs/2307.03172
• LLMLingua: prompt compression with preserved task performance (arXiv:2310.05736): https://arxiv.org/abs/2310.05736
• MemGPT: memory tiers for long conversations and documents (arXiv:2310.08560): https://arxiv.org/abs/2310.08560
• LangChain — Context engineering for agents: https://blog.langchain.com/context-engineering-for-agents/
• Model Context Protocol — specification (tools, resources, transports): https://modelcontextprotocol.io/specification/2026-07-28

Method note

The code in this article is not transcribed. Each block was cut directly out of the slice body returned by the SmartGate slice API and then re-asserted byte-for-byte as a substring of that body before publication; the first line inside every fence records the file and the exact source lines. Symbols were pinned with whole-name containment (rule A level 2) and confirmed by a server-side slot-proof call before any of them entered the text. Where a slice carried a long pydantic field list, the quoted window was narrowed to the logic rather than reproducing the whole handler; nothing was rewritten, and repository-relative paths are shown as they are.

Slice provenance

#	SERP keyword	Symbol	File	Source lines	How it was pinned	sha256(12)
1	smart_context_gate compress the context window before the model call	smart_context_gate	backend/smartgate/api/mcp.py	150–174	rule A L2 → slot-proof	39c700a39edd
2	resolve_compress_ratio resolve the effective compression ratio	resolve_compress_ratio	backend/smartgate/core/effective_settings.py	69–74	rule A L2 → slot-proof	fa11ceea7701
3	splitForPlaygroundCompress split long input before compression	splitForPlaygroundCompress	lib/smartgate/playground-content.ts	113–166	rule A L2 → slot-proof	270a1ccfefed
4	flushSegment flush a compression segment	flushSegment	lib/smartgate/playground-content.ts	135–140	rule A L2 → slot-proof	d2656cf27e56
5	_truncate_pipeline_text truncate pipeline text when the budget is tight	_truncate_pipeline_text	backend/smartgate/core/pipeline.py	176–180	rule A L2 → slot-proof	68e07702bc1b
6	smart_dedup semantic dedup of overlapping context passages	smart_dedup	backend/smartgate/api/mcp.py	176–196	rule A L2 → slot-proof	22b01981f934
7	find_representative find the representative passage in a duplicate cluster	find_representative	backend/smartgate/modules/dedup/algorithm.py	326–352	rule A L2 → slot-proof	11f6bd2056df
8	compute_candidate_limit compute the dedup candidate limit	compute_candidate_limit	backend/smartgate/modules/dedup/utils.py	88–113	rule A L2 → slot-proof	9800d86e921c
9	get_pure_token count pure tokens inside the compressor	get_pure_token	backend/smartgate/modules/context_gate/utils.py	105–111	rule A L2 → slot-proof	d0f34a4f3bd0
10	smart_memory move agent history into team memory	smart_memory	backend/smartgate/api/mcp.py	280–300	rule A L2 → slot-proof	f9c4d58b5501
11	checkQuota monthly token quota check per team	checkQuota	lib/usage/index.ts	101–129	rule A L2 → slot-proof	cf8c0cdcf082
12	smart_pipe multi step pipeline so context is not refetched	smart_pipe	backend/smartgate/api/mcp.py	343–387	rule A L2 → slot-proof	91ee6d5bb70b

Every fenced block above was cut from the slice body and re-asserted against it byte-for-byte before publication. 12 of 12 sections pinned, 0 abstentions, 0 misses.