Agentic Endpoints

Answer a question from your meetings

meetings_summarize

Read-onlyIdempotent

Ask a plain-language question and get a written answer grounded in the user's own meeting transcripts, with the meeting id cited after each claim. Prefer this over meetings_search when the user wants an ANSWER ('what did we decide about pricing?', 'who owned the migration?') rather than a list of excerpts to read themselves. Pass the question as the user asked it -- this is NOT FTS5 syntax, and the terms actually searched come back as terms so you can check the retrieval. IMPORTANT: a 'no_matches' status means no answer was generated, and 'searched_meetings': 0 means every meeting is private and NOTHING was read -- neither is evidence the topic was never discussed, and you must not report it as such. Meetings imported as private are encrypted and can never contribute to an answer; private_meetings_skipped says how many were left out. A 'truncated' meeting in consulted had only part of its transcript read, so its silence on a point is not evidence either. Costs $0.050 in USDC on Base, paid via the x402 protocol, or from a credit token — call credits_trial for free credit if you have neither.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max meetings to consult, 1-8 (default 5)
`question`	Yes	The question in plain language, as the user asked it. Not a search expression.
`namespace`	Yes	Isolation scope holding your meetings
`credit_token`	No	Optional. A credit token from credits_trial or /credits/buy. Supplying it pays for this call from that balance, so no x402 payment or wallet is needed.
`namespace_token`	Yes	One-time token issued by the first call that claimed this namespace. Required for every later call.

Output Schema

TableJSON Schema

Name	Required	Description
`model`	No	The model that produced the answer
`terms`	No	The search terms derived from the question
`answer`	No	The grounded answer with [meeting_id] citations, or null when nothing was summarised.
`status`	Yes	'ok' when an answer was generated. 'no_matches', 'unusable_question' and 'content_missing' all mean NO answer was produced and must not be read as the topic being absent.
`question`	No	The question as asked
`consulted`	No	The meetings the answer was actually built from
`ai_generated`	No	True: `answer` was written by an AI model
`searched_meetings`	No	Queryable meetings available to search. 0 means nothing was read.
`private_meetings_skipped`	No	Encrypted meetings that could not contribute to the answer

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 25, 2026

addedOutput schema / properties / ai_generated

Added value: +{
+  "description": "True: `answer` was written by an AI model",
+  "type": "boolean"
+}

Changed1 schema field changedSep 16, 2026

addedInput schema / properties / credit_token

Added value: +{
+  "description": "Optional. A credit token from credits_trial or /credits/buy. Supplying it pays for this call from that balance, so no x402 payment or wallet is needed.",
+  "type": "string"
+}

AddedSep 10, 2026

TDQS

A4.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, and the description adds substantial behavioral context beyond them: no_matches means no answer was generated, zero searched_meetings means everything private and nothing read, private meetings are encrypted and skipped, truncated meetings cannot serve as evidence, and the cost/payment model. No contradiction with annotations exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average but every sentence earns its place: purpose, usage rule, anti-pattern warning, failure semantics, privacy caveat, truncation caveat, and cost. It is front-loaded with the core action and differentiator, then layers critical operational warnings that directly affect how an agent should interpret results.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, the description need not explain return structure, and it instead covers the non-obvious semantics an agent needs to reason correctly: what no_matches and searched_meetings:0 really mean, the limitation of private and truncated meetings, and the payment path. Nothing needed for correct invocation or interpretation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: it clarifies that question is plain language, not a search syntax, that the terms actually searched come back for verification, and that credit_token can pay from a balance obtained via credits_trial or /credits/buy. This supplements structure without repeating it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: ask a plain-language question and get a written answer grounded in meeting transcripts with cited meeting ids. It explicitly differentiates from meetings_search by describing the exact user intent that selects this tool, so an agent can distinguish it without opening the sibling schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use guidance: prefer meetings_summarize over meetings_search when the user wants an answer rather than a list of excerpts to read. It also instructs to pass the question exactly as the user asked it and explicitly warns that this is not FTS5 syntax, which prevents a common misuse.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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