heor-agent-mcp

knowledge_search

Search a project's knowledge base for text matches to find previously-retrieved literature, model runs, and compiled wiki content without re-querying external APIs.

Instructions

Search a project's knowledge base (raw/ and wiki/) for text matches. Returns file paths with line numbers and snippets. Use this to find previously-retrieved literature, model runs, and compiled wiki content without re-querying external APIs.

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": {
      "project": "<project>",
      "query": "<query>"
    },
    "name": "knowledge_search"
  }
}

Input Schema

Name	Required	Description
`project`	Yes	Project ID (must exist)
`query`	Yes	Search query — multi-term searches match ANY term (OR)
`paths`	No	Which subtrees to search. Default: both.
`max_results`	No	Max matches to return (default 20, max 100)
`case_sensitive`	No	Case-sensitive search (default false)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.7Apr 14, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns file paths with line numbers and snippets, which adds useful behavioral context. However, it lacks details on permissions, rate limits, or error handling, leaving gaps 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?

The description is front-loaded with the core purpose, followed by usage guidance, in two efficient sentences with zero wasted words, making it highly concise and well-structured.

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?

Given no annotations and no output schema, the description provides basic purpose and usage but lacks details on return format, error cases, or operational constraints. For a search tool with 5 parameters, this is adequate but has clear gaps in completeness.

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%, so the schema fully documents all parameters. The description adds no additional parameter semantics beyond what's in the schema, such as explaining search logic or result formatting, resulting in the baseline score.

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's purpose with specific verbs ('search a project's knowledge base') and resources ('raw/ and wiki/'), and distinguishes it from siblings like knowledge_read, knowledge_write, and literature_search by specifying it searches previously-retrieved content without external API calls.

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 provides clear context on when to use this tool ('to find previously-retrieved literature, model runs, and compiled wiki content without re-querying external APIs'), which implicitly differentiates it from literature_search. However, it does not explicitly state when not to use it or name alternatives, keeping it from a perfect score.

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

Deploy Server