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kb_read

Access documents from a knowledge base: read full content, extract a section by heading, or force retrieval of large files when only headings are returned.

Instructions

Lit un document du corpus d'une base : document complet, ou une seule section si section est fourni. Au-delà d'un certain volume, un document lu sans section retourne sa table des headings plutôt que son contenu — rappelez alors kb_read avec la section voulue, ou force: true pour tout obtenir. Bases disponibles : aucune.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseYesNom de la base (champ `name` du manifeste).
pathYesChemin du document, relatif au corpus, séparateur `/`.
forceNoRetourne le document entier même s'il est volumineux, au lieu de sa table des headings.
sectionNoTitre du heading à extraire. La correspondance ignore la casse et le formatage markdown inline.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It usefully exposes the non-obvious behavior that large documents return a headings table unless force is used, and it explains the retry path. It does not detail the response format or possible errors, but covers the most important behavioral quirk.

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 three short, purposeful sentences. The main use case is front-loaded, followed by the volume caveat and the current environment note. There is no redundant or filler wording.

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

Completeness4/5

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

Given no output schema and no annotations, the description provides enough to invoke the tool correctly: full-document vs section behavior, the large-document headings fallback, force override, and the fact that no bases are currently available. It is slightly limited by not describing response structure or error cases, but those are not essential for basic usage.

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 parameter semantics are already fully documented in the schema. The description mostly re-contextualizes the behavior of section and force rather than adding new parameter-level meaning, matching the baseline for high schema coverage.

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: 'Lit un document du corpus d'une base' and immediately explains both main modes: full document or a single section. This clearly distinguishes kb_read from sibling tools like kb_list and kb_search.

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 gives concrete usage guidance: for large documents without a section, a headings table is returned, and the agent should retry with the desired section or use force: true. It does not explicitly compare kb_read with sibling alternatives, but the intended context is clear.

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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