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findocs-mcp

findocs-mcp

by singhh879
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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Search financial documents

search_docs

Search financial documents semantically to retrieve relevant chunks ranked by cosine similarity, including source metadata.

Instructions

Semantic search over the indexed financial-docs corpus. Returns the top-k chunks with cosine similarity scores and source metadata.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_docs"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesNatural-language search query
kNoNumber of chunks to return (default from config)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses semantic search nature, output composition (chunks, scores, metadata), and search scope. However, it omits details like potential rate limits, authentication, or read-only nature, but is still fairly transparent for a search tool.

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?

A single sentence that front-loads the core action ('Semantic search') and includes key output information. No wasted words, highly efficient.

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 simple 2-parameter tool, the description adequately covers purpose, output, and scope. However, it doesn't specify the default value for k ('from config' is vague) and lacks detail on 'source metadata,' leaving minor gaps.

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% (both query and k have descriptions). The description adds no extra semantic detail beyond what's in the schema; it merely restates the operation. 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 clearly states the tool performs 'semantic search' over a specific corpus ('financial-docs') and returns 'top-k chunks with cosine similarity scores and source metadata,' distinguishing it from siblings like answer_question (likely answering) and ingest_doc (adding documents).

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?

The description implies usage for retrieval but lacks explicit when-to-use or when-not-to-use guidance compared to sibling tools. No alternatives mentioned, so it's adequate but not explicit.

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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