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lpm-dev

@lpm-registry/mcp-server

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by lpm-dev

lpm_package_context

Evaluate an LPM package before installing by retrieving its condensed metadata, structured API docs, and LLM usage guide in a single call.

Instructions

Get complete context for an LPM package in a single call — combines condensed package metadata (name, version, description, install method, dependencies), structured API docs (functions, classes, types), and LLM usage guide (quickStart, patterns, gotchas). Use this BEFORE installing to evaluate and understand a package. If the package is already installed locally, prefer reading local files directly and use lpm_package_skills for usage patterns instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPackage name in owner.package-name or @lpm.dev/owner.package-name format
versionNoSpecific version to get context for (defaults to latest)
Behavior4/5

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

No annotations were provided, so the description fully discloses the tool's purpose and outputs (metadata, API docs, usage guide), implying a read-only operation. It lacks mention of rate limits or auth, but these are not critical for a context retrieval 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?

Three sentences, each providing distinct value: what the tool does, when to use it, and when to use an alternative. No redundant information.

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?

Given the lack of output schema and annotations, the description adequately explains the tool's purpose and usage context. It could detail the return structure slightly, but the current level is sufficient for correct selection and invocation.

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 fully describes both parameters with 100% coverage. The description adds no additional parameter details beyond what the schema provides, meeting the baseline 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 states the tool retrieves complete package context (metadata, API docs, usage guide) and explicitly differentiates it from sibling tools like lpm_package_skills by specifying when to use each.

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?

Clearly states to use this before installing to evaluate a package, and advises using local files and lpm_package_skills for already installed packages, providing clear when-to and when-not-to guidance.

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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