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topology_search

Search the code topology to find symbols, functions, types, and classes by name, identifier, signature, or docstring. Returns ranked results with kind, file path, and line range.

Instructions

Ranked FTS5 search over the topology index. Finds symbols, functions, types, classes, and other named entities by name, tokenised identifier (camelCase/snake_case), qualified name, signature, or docstring. Results include kind, file path, line range, match field, score, and optional snippet. Source is 'topology' (approximate; use search_in_files for exact filesystem matches). Code-structure corpus only — for ranked discovery that also spans docs and memories, use workspace_search (this tool is one of its backends). Returns a clear message when the index is disabled or empty.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindsNoOptional filter by node kinds: function, method, type, class, constant, variable, import, package, test.
limitNoMaximum number of results to return. Default 20.
queryNoSearch query. Terms are OR-matched against symbol names, tokenised identifiers (camelCase/snake_case split), qualified names, signatures, and docstrings.
rerankNoRe-rank FTS5 results by semantic similarity to the query (needs [semantics] enabled + an API key). Defaults to the [semantics].enabled config; pass false to force the plain FTS5 ranking, true to force re-rank when configured.
languageNoOptional filter by language (e.g. 'go', 'python').
include_snippetsNoInclude a short snippet showing the matching text. Default true.
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses important behaviors: the source is approximate, results include specific fields, and a clear message is returned when the index is disabled/empty. It does not mention side effects (none expected) or auth requirements, but these are not critical for a read-only search.

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 efficiently written with no wasted words, but it is somewhat dense, packing many details into several sentences. It front-loads the primary purpose and then adds scope and alternative guidance. Slightly long but appropriately so for the information it conveys.

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

Completeness5/5

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

Despite having 6 params and no output schema, the description is complete: it explains what results include (kind, file path, line range, match field, score, snippet), the approximate source, the corpus limitation, and fallback behavior for disabled/empty index. The schema covers parameter details, so nothing critical is missing.

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% for all 6 parameters, so the schema already documents each parameter. The description adds context about the overall search behavior (ranked, FTS5) but does not explain individual parameters beyond the schema. This matches the baseline of 3 for high schema coverage.

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 ranked FTS5 search over the topology index for named entities, listing specific searchable attributes (name, tokenised identifier, qualified name, signature, docstring). It distinguishes itself from siblings by naming search_in_files for exact filesystem matches and workspace_search for cross-corpus discovery.

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

Usage Guidelines5/5

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

The description gives explicit guidance on when to use this tool vs alternatives: 'use search_in_files for exact filesystem matches' and 'use workspace_search' for discovery spanning docs and memories. It also clarifies the corpus scope ('Code-structure corpus only'), making the decision context clear.

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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