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Search codebases using natural language, code, or exact symbols. Choose hybrid, BM25, or semantic mode; filter by language or file paths for precise results.

Instructions

Search a codebase with a natural-language, code, or exact-symbol query.

Use hybrid by default, bm25 for exact identifiers and literals, and semantic for conceptual behavior. Optional language and filter_paths filters narrow the index when the agent already knows where to look. Use source for local paths or Git URLs and limit for result bounds. Results include formatted text for context injection and structured fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
alphaNoOptional hybrid semantic weight. Omit to let SIFS choose from query shape.
explainNoInclude per-result ranking evidence such as BM25 rank, semantic rank, alpha, and boosted score.
filter_languagesNoOptional exact language labels to search, such as rust or typescript.
filter_pathsNoOptional repository-relative file paths to search.
limitNoMaximum number of ranked chunks to return.
modeNoUse hybrid by default, bm25 for exact symbols/literals, and semantic for conceptual queries.hybrid
profileNoSaved profile to use for source and search defaults.
queryYesNatural language or code query.
sourceNoGit URL or local path to index and search.
Behavior3/5

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

No annotations present, so description must carry behavioral info. It discusses modes, filters, and result format ('formatted text...'), but omits side effects, latency, or permissions. Adequate but not thorough.

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?

Description is well-structured: starts with purpose, then usage guidance, then parameter roles. No fluff, but slightly lengthy at three sentences.

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

Completeness4/5

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

For a search tool with 9 params and no output schema, the description covers query types, modes, filters, and result hints. Lacks explicit return structure details, but is largely sufficient.

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 coverage is 100%, baseline 3. Description adds minor context (e.g., 'let SIFS choose from query shape' for alpha), but largely restates schema. Does not significantly enhance meaning.

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 starts with a specific verb-resource pair ('Search a codebase') and lists query types (natural-language, code, exact-symbol), clearly distinguishing it from siblings like get_chunk or find_related.

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?

Provides explicit guidance on mode selection ('hybrid by default, bm25 for exact identifiers...') and optional filters, but does not specify when not to use the tool.

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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