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list_knowledge_files

Read-onlyIdempotent

List knowledge files attached to an account, such as price lists, terms, and product sheets. See which registered files are available as fact sources for replies.

Instructions

List the knowledge files the user attached to an account (price lists, terms, product sheets...) — the intended source of facts for replies. Returns a list of {name, path, kind, size}. Only paths the user explicitly registered are visible: this is not a filesystem browser. Read the text of one with read_knowledge_file. Read-only, local.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_idNoAccount to operate on (integer id from list_accounts). Omit or null = the user's active account.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

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

  1. Changed1 schema field changedv0.3.0
    • addedInput schema / properties / account_id / description
      Added value: +"Account to operate on (integer id from list_accounts). Omit or null = the user's active account."
  2. First observedv0.1.3

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context beyond this: it specifies the output structure ({name, path, kind, size}) and clarifies that only explicitly registered paths are visible, which is a behavioral nuance not captured by annotations. The statement 'Read-only, local' reinforces the read-only nature.

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 front-loaded with the core purpose, then adds scoping constraints, output format, and a pointer to the sibling tool. Every sentence adds value with no redundancy.

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 tool is a simple read-only list with a fully described parameter and an output schema (indicated by 'Has output schema: true'), the description covers everything needed: what it returns, scope, and how to proceed. No critical information 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?

The single parameter account_id is fully documented in the schema (including how to omit or use null). The description adds no additional meaning beyond the schema, so it meets the baseline of 3 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?

States a specific verb (List) and resource (knowledge files attached to an account), and clarifies the intended purpose ('the intended source of facts for replies'). It explicitly contrasts with a filesystem browser and names the sibling read_knowledge_file, making it easy to distinguish.

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?

Provides clear guidance on when to use this tool: to see the user's registered knowledge files. It states what it is not ('not a filesystem browser') and points to read_knowledge_file for reading the text, effectively routing the agent to the correct tool for the next step.

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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