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grooverLab

fable MCP server

fable_thread

Retrieves exact conversation thread turns by prompt ID, preserving original order. Ideal for recovering precise details after a search.

Instructions

Read one conversation thread VERBATIM (user → assistant → tool turns, in order) under a token budget — the exact past turns, not a paraphrase. Use after fable_search (or with a known prompt_id) to recover precise detail a summary would have lost. Bulky tool results are elided with block pointers (fetch via fable_block).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prompt_idYes
budgetNo
rawNo
Behavior4/5

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

Without annotations, the description effectively communicates the tool's read-only behavior: it reads verbatim ordered turns under a token budget, and truncates bulky results with block pointers. This adequately discloses key behavioral traits beyond what annotations would provide.

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-loading the core function and following with essential usage guidance. Every sentence adds value without redundancy or unnecessary information.

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

Completeness3/5

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

Given zero schema descriptions, no output schema, and no annotations, the description covers the main purpose and usage context but lacks details on the 'raw' parameter, output format, and exact budget truncation behavior. It is adequate but not fully complete for an agent to predict all behaviors.

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

Parameters2/5

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

The input schema has 3 parameters with 0% description coverage, so the description must compensate. It mentions 'token budget' (budget) and hints at truncation, but does not explain the 'raw' parameter or provide details on prompt_id usage beyond being required. The description adds minimal semantic value beyond the schema.

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 the verb 'read' and the resource 'conversation thread ver batim', specifying it returns exact past turns, not a paraphrase. It distinguishes itself from fable_search by indicating that fable_search returns summaries, making the tool's unique purpose explicit.

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 explicitly advises using this tool after fable_search or with a known prompt_id to recover detail lost in summaries. It also mentions that bulky results are elided with block pointers (fetch via fable_block). This provides clear context on when and how to use the tool, though it could be more explicit about when not to use it.

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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