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query_registry

Query the local manifest registry to discover package exports, symbol kinds, and side effects for npm dependencies.

Instructions

Query the local manifest registry for package export information and side effects.

The registry contains pre-analyzed manifests for npm dependencies. Each manifest describes a package's API surface: exported symbols, their kinds, and side effects.

Use this when you need to:

  • Know what a package exports: query_registry(package="graphql") → 216 exports

  • Check effects of a specific function: query_registry(package="yaml", symbol="parse") → PURE

  • Understand a dependency's API without reading its source code

  • Verify if a package is in the registry: query_registry(package="express") → not found

Returns: package metadata (purl, source_type, confidence), and either:

  • A specific export (when symbol is given)

  • All exports summary (when only package is given)

  • Full registry index (when neither is given)

source_type values:

  • "compiled_js" — standard npm package, fully analyzed

  • "source" — TypeScript source, fully analyzed

  • "minified" — bundled output (esbuild/webpack), exports not statically resolvable

  • "dts_only" — type declarations only, not in registry

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageNoPackage name (e.g., "graphql", "@anthropic-ai/sdk"). Omit to list all packages.
symbolNoExported symbol name (e.g., "parse", "GraphQLSchema"). Requires package.
Behavior4/5

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

With no annotations, the description carries full burden. It explains registry contents, source_type values, and return format. It lacks details on authentication or rate limits, but those are less critical for a read-only query tool.

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?

The description is concise at around 150 words, with clear bullet points for usage and return types. Every sentence adds value, and the structure is front-loaded.

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?

The description explains return format and source_type values despite no output schema. It lacks explicit error handling, but for a simple query tool with two parameters, it is mostly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, and the description adds value with examples and clarifies behavior when parameters are omitted. It explains that 'symbol' requires 'package' and the three output modes.

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?

The description clearly states it queries the local manifest registry for package exports and side effects, with specific examples. However, it does not explicitly differentiate from sibling tools like query_graph or semantic_search.

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?

The description provides a clear list of when to use the tool, including knowing exports, checking effects, and verifying registry presence. It does not mention when not to use it or alternatives.

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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