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Zackriya-Solutions

MCP-Markdown-RAG

search_documents

Find semantically relevant markdown documents in your local files by querying. Enter a query to retrieve matching documents using vector-based search.

Instructions

Search for semantically relevant documents based on query

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoNumber of documents to return
queryYesQuery to search for
Behavior3/5

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

With no annotations, the description carries the burden. It reveals that search is semantic ('semantically relevant'), but does not disclose prerequisites like prior indexing, result ordering, or return structure. Adds some value but misses important behavioral context.

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?

Single sentence, front-loaded with the verb and resource, zero filler. Efficient and appropriately sized for a simple search tool.

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

Completeness3/5

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

For a two-parameter tool with no output schema, the description is adequate but leaves gaps: it does not state that documents must be indexed first, what the return format looks like, or any constraints. However, the basic purpose is clear, and sibling tools provide some context.

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%, with both query and k already documented. The description adds no additional parameter meaning beyond restating that search is based on query, so baseline 3 is appropriate.

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 uses a specific verb 'search' plus resource 'documents' and qualifier 'semantically relevant', clearly distinguishing it from siblings index_documents and clear_index. It states exactly what the tool does.

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 indicates usage for semantic search, which is implied rather than explicit. It does not explicitly mention when not to use it or name alternatives, but the sibling names and clear context provide adequate guidance.

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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