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Agent context index

Use Teammately docs safely from AI agents, retrieval tools, and coding assistants.

Agent context index

Use this page to choose the smallest Teammately docs context package that can answer a product, workflow, object, troubleshooting, or code-assistance question. This page is about retrieval surfaces and source boundaries, not autonomous agent behavior policy.

Task intent

Select a context source before reading or summarizing Teammately docs. Agents should start with the smallest pack or retrieval query that matches the task, then load page-local context only when the answer needs exact wording.

The decision rule is conservative: load less context first, cite stable public pages, and stop before inferring product contracts from draft docs or internal file paths.

Decision checkpoint

Agent taskStart withEscalate only when...
Route a human to the right docs/docs/llms.txt or machine mapsThe user needs page-local wording.
Explain category or first path/docs/llms-core.txtA specific workflow page is needed.
Answer an operating question/docs/llms-operating.txt or /api/docs/contextThe answer depends on exact source confidence.
Explain object semantics/docs/llms-reference.txtExact page retrieval is needed.
Diagnose a symptom/docs/llms-recovery.txtThe symptom spans multiple artifacts.
Work through an MCP clientMCP searchExact page or block Markdown is needed through fetch.
Build an index or audit/docs/llms-full.txtFull-corpus context is intentionally required.

Required context

Context pack strategy

  1. Start with /docs/llms.txt to understand the docs map and source-confidence policy.
  2. Use one focused pack: core for positioning, operating for workflows, reference for objects, or recovery for symptoms.
  3. Use /api/docs/context for query-scoped context when the task is narrow.
  4. Load /docs/<slug>/docs/llms-full.txt only when a specific page needs full context.
  5. Treat /docs/llms-full.txt as an intentional full-corpus dump for indexing or exhaustive review, not as the default context.

MCP retrieval strategy

  1. Call search with the user's question and an optional kind or product-area filter.
  2. Read the returned search mode and build metadata. Semantic retrieval can fall back to lexical retrieval without making results invalid.
  3. Use cited blockId, pageId, or slug with fetch before quoting exact guidance or applying an instruction.
  4. Use list only for navigation or filtered inventory; it is not a substitute for page content.
  5. Treat AI Overview text as a cited routing aid. The referenced blocks and fetched page remain the evidence boundary.

The MCP resources teammately-docs://manifest, teammately-docs://docs/{slug}, and teammately-docs://blocks/{blockId} provide the same active corpus at manifest, page, and block granularity.

Agent answerability check

Before answering, verify...If not...
The cited page is stable and public.Say the source boundary is not public rather than quoting draft files.
Source confidence matches the claim strength.Downgrade the answer or ask for human confirmation.
The route answers the user's task without loading unrelated packs.Use search or /api/docs/context before full-corpus context.
MCP overview claims resolve to returned block references.Fetch the cited block or page before relying on exact wording.
The claim is not about API, auth, billing, compliance, retention, deployment, or provider handling unless a source-backed page says so.State that the docs do not currently confirm the contract.

Available routes

Allowed assumptions

  • Stable public docs are the public source of truth for published guidance.
  • Draft and removed pages are intentionally excluded from public routes and root context packs.
  • Source confidence metadata tells the agent how strongly to treat a claim.
  • Page-local routes are useful for exact wording; root packs are useful for routing and broad orientation.

Forbidden assumptions

  • Do not load draft or removed docs from repository files as public facts.
  • Do not treat /docs/llms-full.txt as mandatory for every answer.
  • Do not infer public API, auth, rate-limit, compliance, deployment, billing, or integration guarantees from internal evidence paths.
  • Do not collapse doctrine-backed positioning into code-backed behavior, or code-backed product behavior into public external contracts.

Related human docs

Concept / Learning ManualWhat is Teammately?Understand Teammately as correctness infrastructure for building trustworthy specialist AI with expert judgment and AI agents.Concept / Operating ManualTask indexRoute correctness work to the current project foundation, benchmark workspace, evaluation, or improvement surface.Reference / Reference LibraryReference libraryLook up Teammately objects, states, permissions, metadata, schemas, and source-confidence boundaries.Concept / Recovery LibraryTroubleshootingDiagnose common Teammately setup, upload, review, classification, and benchmark issues.

Related reference docs

Reference / Reference LibraryAgent instructionsRules coding agents should follow when using Teammately public docs as source context.Reference / Reference LibraryObject modelUnderstand how project foundations, benchmark artifacts, contributions, evaluations, and improvement sessions connect.Reference / Reference LibraryPermissionsUnderstand the user-facing permission boundaries for projects, reviewers, settings, and expert UI access.

Safe operating boundaries

Agents should answer from stable public pages and cite the page URL. If a needed claim is only draft, inferred, or absent from the context packs, say the source boundary is unconfirmed and ask for human confirmation.

When a task needs exact behavior, prefer /api/docs/page?slug=<slug> or page-local markdown over a broad pack. When a task needs a route into the docs, prefer /docs/llms.txt, focused packs, search, and machine maps before loading full-corpus context.

Source confidence

Docs-backed: this page describes routes and files implemented in the docs repository. The source_refs frontmatter lists the docs loaders, llms generators, and machine-readable route files.

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Related docs

Reference Library Agent instructions Rules coding agents should follow when using Teammately public docs as source context. Reference Library Object model Understand how project foundations, benchmark artifacts, contributions, evaluations, and improvement sessions connect. Reference Library Reference library Look up Teammately objects, states, permissions, metadata, schemas, and source-confidence boundaries.
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