Skip to main content
Glama

repo_query_knowledge_graph

Query repository knowledge graph to locate files, functions, classes, modules, or dependencies by name or type. Get structured entity information for code analysis.

Instructions

Query the repository knowledge graph by entity name or type (file, function, class, module, dependency).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesAbsolute path to the repository root directory.
typeNoFilter by entity type.
queryNoOptional search string to filter entity names.
Behavior2/5

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

No annotations are provided, so the description must carry full behavioral burden. It fails to mention whether the knowledge graph must be pre-built, whether the operation is read-only, or what the return format is. The only behavioral detail is the query mechanism (by name/type), which is minimal.

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, front-loaded sentence with no filler or redundancy. It immediately states the action and resource, making it highly scannable and appropriately sized.

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 straightforward query tool, the description is minimally viable but lacks important context such as prerequisites (e.g., the knowledge graph must be built first) and output expectations. Without annotations or an output schema, the description would benefit from additional detail, but it is not entirely inadequate.

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?

The input schema already provides descriptions for all three parameters (100% coverage), so the baseline is 3. The description adds the entity type list, but this is already present in the enum for the 'type' parameter, so no substantial additional meaning is provided.

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?

Description uses a specific verb 'Query' with a clear resource 'repository knowledge graph' and enumerates entity types (file, function, class, module, dependency). This clearly distinguishes it from siblings like repo_build_knowledge_graph (which builds) and repo_ask_codebase (which likely answers natural language questions).

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 explicit guidance on when to use this tool versus alternatives such as repo_ask_codebase or repo_analyze_repository. The description only states what it does without any contextual hints about prerequisites or preferred scenarios.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dawnmcp/DawnMCP-1'

If you have feedback or need assistance with the MCP directory API, please join our Discord server