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codegraph

codegraph

by mooyee0929
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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search_nodes

Locate functions, classes, or modules in a code graph by matching names, signatures, or docstring keywords so agents can trace relationships and dependencies without scanning files.

Instructions

Find functions, classes and modules by name, signature or docstring keyword.

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_text": "<query_text>"
    },
    "name": "search_nodes"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
query_textYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.8/5.0
Behavior2/5

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

Annotations are absent, so the description carries the full behavioral burden, yet it discloses nothing about matching behavior (substring vs regex vs ranking), result ordering, pagination, or read-only nature. Mentioning the three match surfaces helps slightly but leaves the operation's behavior largely opaque.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single clean sentence with the core purpose front-loaded and no wasted words. Efficient, though its brevity is also the source of the coverage gaps elsewhere.

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?

The output schema exists, so return values need not be explained, and for a two-parameter search tool the description is minimally serviceable. However, with zero annotation coverage and no param documentation, an agent lacks enough to invoke it confidently (limit semantics, match/order behavior).

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?

Schema description coverage is 0%, so the description must compensate, and it only partially does. It clarifies what query_text is matched against, but the 'limit' parameter (default 20) is entirely unexplained in both schema and description.

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?

States a specific verb 'find' and a concrete resource set ('functions, classes and modules'), and enumerates the match surfaces (name, signature, docstring keyword). This distinguishes it from get_node's single-node retrieval, but it never names an alternative sibling explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to reach for search_nodes versus query_graph, get_node, or get_architecture_overview. The agent must infer usage from the verb alone; no exclusions or alternatives are given.

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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