claudette

read_passage

Retrieve a cited passage verbatim with surrounding context to verify quotes or citations. Use after searching the corpus.

Instructions

Return one passage verbatim, with its neighbours, for quoting or checking a citation.

Use after search_corpus when you need the surrounding text. Not for finding
passages — it takes a ref, not a question.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`ref`	Yes	A citation ref from search_corpus, e.g. 'follett-new-state§412'.
`context`	No	How many neighbouring passages to include either side.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No
`status`	Yes	ok — passages found that match most of the question's terms. weak — something matched, but thinly; say so if you use it. no_coverage — the corpus does not speak to this. Say that. Do not answer from elsewhere. not_found — a specific reference did not resolve.
`constraint`	No		Answer only from these passages. Quote or closely paraphrase, and cite each claim as [Author, Title §n]. If the passages do not speak to the question, say so plainly rather than filling the gap from elsewhere.
`provenance`	No		Every passage is from a work by a named woman author, in the public domain, via Project Gutenberg.
`limitations`	No	Part of the answer, not a disclaimer.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 18, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals that the tool returns the passage 'verbatim' and includes 'neighbours', which defines the output behavior, and implies a read-only operation. It also clarifies that the input is a reference, not a free-text query, which is a behavioral constraint. It does not cover error handling or edge cases, but for a simple read tool this is adequate.

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 long, with the primary purpose and scope front-loaded. The second sentence provides usage guidance and exclusion, with no redundant filler. Every phrase earns its place.

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?

The tool has an output schema, so return values are defined elsewhere. The description covers what the tool does, when to use it, and how it differs from its sibling, and the parameters are fully documented in the schema. For a simple read operation, nothing an agent needs to call it correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100%, so both parameters are already documented with examples and constraints. The description adds minimal additional parameter meaning: 'with its neighbours' indirectly aligns with the context parameter, but this is already in the schema. The phrase 'takes a ref, not a question' reinforces the ref parameter's type, but that is also implied by the schema. Thus the description does not add significant 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 states a specific verb ('Return') and a precise resource ('one passage verbatim, with its neighbours') and names the intended use case ('for quoting or checking a citation'). It explicitly differentiates from the sibling tool search_corpus by clarifying that it does not find passages, so an agent can distinguish them without inspecting schemas.

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?

It gives a clear when-to-use instruction ('Use after search_corpus when you need the surrounding text') and a when-not-to-use exclusion ('Not for finding passages — it takes a ref, not a question'). This directly routes the agent to the correct tool and explains the prerequisite that the ref comes from search_corpus.

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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