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RAGDown

by cubicecho
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TypeScript
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Search notes

ragdown_recall
Read-only

Find relevant sections in Markdown notes using combined semantic and keyword search. Returns file paths, line ranges, heading breadcrumbs, and similarity scores so you can locate source material before answering.

Instructions

Hybrid (semantic + keyword) search over the user's Markdown notes. Returns the most relevant sections with file path, line range, heading breadcrumb and cosine similarity (above ~0.8 is usually on topic). Use it before answering anything the notes may cover; follow up with ragdown_read_doc for the surrounding text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagNoOnly search notes with this tag (frontmatter tags or inline #tags); 'project' also matches 'project/alpha'
queryYesWhat to look for, as a question or keywords
top_kNo
formatNotext
max_charsNotext format: characters per hit before it is clipped (0 = never)
path_prefixNoOnly search files under this subfolder, relative to the notes root, e.g. 'projects/'

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv5.2.0
    • changedInput schema / properties / path_prefix / description
      Previous value: -"Only search files under this folder, relative to the notes root, e.g. 'projects/'"New value: +"Only search files under this subfolder, relative to the notes root, e.g. 'projects/'"
    • addedInput schema / properties / tag
      Added value: +{
+  "description": "Only search notes with this tag (frontmatter tags or inline #tags); 'project' also matches 'project/alpha'",
+  "type": "string"
+}
  2. First observedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, so no contradiction. The description adds value beyond annotations by disclosing the output format (file path, line range, heading breadcrumb, cosine similarity) and a heuristic threshold ('above ~0.8 is usually on topic'). It does not describe pagination or sorting, but for a safe read-only search 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?

Two sentences, front-loaded with the core purpose, then the usage rule. No filler or repetition of schema content. The behavioral note about similarity is concise and useful.

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?

For a search tool with no output schema, the description covers when to use it, what it returns, and what to do next. It does not explain format json vs text or max_chars, but those are adequately described in the schema. The main gap is not mentioning whether results are sorted by score, but the similarity note implies ranking. Overall complete enough for correct invocation.

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 67% (4 of 6 params have descriptions). The description itself does not add details for the undocumented top_k and format parameters, though it implies ranking relevance. It clarifies query semantics via 'hybrid (semantic + keyword)', but leaves parameter-specific meaning mostly to the schema. This is adequate, not exceptional.

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 names a specific verb ('search'), resource ('the user's Markdown notes'), and mechanism ('hybrid semantic + keyword'). It also lists what the tool returns (sections with file path, line range, breadcrumb, similarity) and distinguishes it from the sibling ragdown_read_doc, which is for surrounding text.

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 states exactly when to use the tool ('before answering anything the notes may cover') and provides a follow-up action ('follow up with ragdown_read_doc for the surrounding text'). This gives the agent a clear decision rule without having to infer from sibling schemas.

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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