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add_local_docs

Index local HTML, Markdown, PDF, and TXT files from a folder for semantic search. Extracts, chunks, and embeds text so AI agents can retrieve relevant documentation.

Instructions

Index local documentation files from a folder on disk.

Walks the folder, finds supported files (HTML, Markdown, PDF, TXT), extracts text, chunks, embeds, and stores them for semantic search.

Args: name: Short identifier for this collection, e.g. "internal-api". path: Absolute or relative path to the folder on the server. recursive: If true, descend into subdirectories (default true).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
pathYes
recursiveNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does disclose the internal pipeline (walk, extract, chunk, embed, store) and supported formats, which is real value. It omits key behavioral facts: whether re-indexing the same name overwrites or duplicates, whether the call is synchronous or queues a job (the get_ingest_status sibling strongly implies asynchronous), and any permission requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the purpose, then the pipeline, then an Args block; no filler sentences. It slightly duplicates the schema's 'default true' for recursive, but that is minor.

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?

An output schema exists, so return values need no explanation, and the description covers purpose, mechanics, formats and all three parameters. The remaining hole is operational context: async/job semantics tied to get_ingest_status and re-index behavior, which an agent about to invoke this tool would benefit from.

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?

Schema description coverage is 0%, so the description must compensate and largely does: it explains name as a short identifier with an example, path as absolute or relative on the server, and recursive's descent behavior and default. The only gap is that it doesn't state the uniqueness/overwrite semantics of 'name'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence gives a specific verb ('Index') and resource ('local documentation files from a folder on disk'), and the pipeline sentence clarifies what indexing entails. The word 'local' implicitly separates it from add_documentation, but no sibling is named, so the differentiation is inferred rather than stated.

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

Usage Guidelines3/5

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

Usage is implied by 'local ... on disk' and the supported file list, which tells the agent this is for already-present files rather than fetched sources. However, there is no explicit when-to-use versus add_documentation or search_documentation, and no mention that get_ingest_status is the follow-up for tracking the job.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.