Skip to main content
Glama

search_codebase

Search indexed codebase files by regex pattern or natural-language description. Semantic mode returns ranked hits with signatures and docstrings to reduce token usage.

Instructions

Regex (default) or semantic (semantic=true) search across indexed files.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias de `pattern`. Regex pattern (regex mode) or natural-language description (semantic mode).
queryNoAlias de `pattern`. Regex pattern (regex mode) or natural-language description (semantic mode).
regexNoAlias de `pattern`. Regex pattern (regex mode) or natural-language description (semantic mode).
patternNoRegex pattern (regex mode) or natural-language description (semantic mode).
projectNoProject name/path (default: active).
semanticNoIf true, interpret `pattern` as a description and rank symbols by embedding cosine similarity. Returns enriched hits with signature/docstring/score. Default false (regex).
max_resultsNoMaximum number of results to return (default 100, 0 = unlimited).
ignore_generatedNoSkip generated/minified files (default true). Regex mode only.
Behavior3/5

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

No annotations exist, so the description carries the behavioral disclosure burden. It names the two modes and defaults but does not detail side effects, authentication needs, or result handling. The schema fills some gaps, but the description alone 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.

Conciseness4/5

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

The description is a single sentence that front-loads the key functionality (regex vs semantic search). It is concise and wastes no words, though it could include more context without losing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 8 parameters, many sibling tools, and no output schema, the description lacks details on return format, pagination, and result structure. The schema parameter descriptions hint at enriched hits, but agents need more explicit context to use the tool effectively.

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 already explains parameters thoroughly. The tool description adds no extra semantic value beyond restating the two modes, which is baseline.

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 'search across indexed files' with two explicit modes (regex default, semantic with semantic=true). It is specific and distinguishes from sibling tools that search other resources (e.g., memory_search, ts_search).

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?

The description provides no guidance on when to use this tool vs alternatives like ts_search or memory_search. It lacks context for selection criteria, prerequisites, or exclusions.

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/Mibayy/token-savior'

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