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mcp-intelligence-context

mcp-intelligence-context

by LeoChimal09
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search_code

Find files and symbols in a codebase using natural language queries. Get ranked matches with reasons, avoiding full repository scans.

Instructions

Search the indexed repository for files/symbols relevant to a natural language or keyword query (e.g. "authentication middleware", "parseConfig"). Returns ranked file hits with the matching symbol/reason — much cheaper than scanning the whole repo.

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_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
top_kNo
repo_rootNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It does reveal the main output behavior—ranked file hits with matching symbol/reason—and the cost advantage. However, it does not mention whether the repository must already be indexed, how repo_root is scoped, or any failure/edge behavior.

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 compact and front-loaded: it states the action, example usage, return behavior, and cost benefit in two efficient sentences. There is no fluff or redundant restatement of the tool name.

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?

This is a fairly simple tool and the description covers the core purpose and return shape adequately. Still, it leaves important invocation details undocumented, especially the semantics of top_k and repo_root, plus the implied prerequisite that the repository must be indexed first.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds useful meaning for the query parameter with natural-language/keyword guidance and examples, but top_k and repo_root are not explained at all. Since schema description coverage is 0%, the description was expected to compensate for all parameters but only covers one meaningfully.

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 description clearly states the tool searches the indexed repository for files/symbols matching a natural language or keyword query and returns ranked file hits with matching symbols/reasons. It is specific about the resource and output, though it does not explicitly distinguish it from siblings like get_relevant_context.

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 gives clear context for when to use the tool: to find relevant files/symbols via semantic or keyword search. It also notes the tool is much cheaper than scanning the whole repo, which is useful guidance, though no explicit when-not-to-use or alternative routing is provided.

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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