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ClintMoody

deep-think-mcp

by ClintMoody

summarize_session

Generate a deterministic extractive digest of committed thoughts from a reasoning session, covering the current stage or all stages.

Instructions

Deterministic extractive digest of this session's committed thoughts. scope="stage" (default) covers only the current stage; scope="all" covers every stage. No LLM calls -- this is text extraction, not summarization by inference.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNostage
session_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Even without annotations, the description discloses key behavioral traits: deterministic, extractive, no LLM calls, and scoping behavior. It is transparent about what the tool does and does not do.

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 two sentences, front-loaded with the main verb and resource, and contains no unnecessary words. Every sentence adds value.

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 presence of an output schema, the description does not need to explain return values. It covers all relevant aspects: purpose, parameters, behavioral traits, and scope.

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?

With 0% schema description coverage, the description adds value by explaining the scope enum and default. For session_id, it is standard and self-explanatory, so the description is adequate.

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 clearly states it produces a deterministic extractive digest of committed thoughts, and distinguishes it from LLM-based summarization. It specifies the scope parameter behavior, making the purpose unambiguous.

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

Usage Guidelines4/5

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

The description tells when to use the tool (to get a digest of committed thoughts) and provides details on scope. It does not explicitly list alternatives, but the purpose is clear enough to guide usage among siblings.

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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