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should-i-use

by io.github.mrkeyoor

Audit dependencies

audit_dependencies
Read-onlyIdempotent

Audit a project's dependencies for unmaintained packages, unstable APIs, and documented reasons to switch. Pass the contents of package.json, requirements.txt, or pyproject.toml. Returns only the dependencies worth a decision, not a report on every line. Run this before working in an unfamiliar codebase.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
manifestYesContents of package.json, requirements.txt, pyproject.toml, or a newline-separated list of package names
ecosystemNoOptional hint when the manifest format is ambiguous

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerYesThe clamped text answer (hard limit ~500 tokens)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare read-only and non-destructive behavior. The description adds useful behavioral context: it accepts manifest contents, filters to only notable dependencies, and is intended as a pre-work audit. No contradiction with annotations, and the added traits go beyond what annotations express.

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?

Four concise, front-loaded sentences. The first states the core purpose, the second specifies input format, the third clarifies output scope, and the fourth provides usage timing. No fluff.

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?

Given the output schema and annotations already cover return values and safety, the description is complete: it explains what it does, what to pass, what to expect, and when to run it. No significant gaps for a 2-parameter tool.

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 fully documents 'manifest' and 'ecosystem'. The description repeats the accepted manifest formats but does not add new parameter semantics beyond the schema, so baseline of 3 is appropriate.

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 'Audit a project's dependencies' with specific criteria (unmaintained packages, unstable APIs, documented reasons to switch). It differentiates from siblings by focusing on dependency health and returning only decision-worthy items, not full reports.

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?

Gives explicit context: 'Run this before working in an unfamiliar codebase.' It implies a focused use case and sets expectations for output. However, it does not explicitly name alternative tools or when not to use it, though the adjacent sibling names suggest related advisory tools.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct phase: deciding on a library (should_i_use, pick_library, alternatives), learning usage (how_do_i, docs_link), and auditing existing dependencies (audit_dependencies). There is no meaningful overlap between any two tools, as they clearly differentiate by input (task vs. library) and output (recommendation vs. snippet vs. docs).

Naming Consistency3/5

The names are all snake_case but mix imperative verbs (audit_dependencies, pick_library), nouns (alternatives, docs_link), and question phrases (how_do_i, should_i_use). This makes the set readable but not predictably patterned; an agent cannot anticipate a consistent verb_noun structure.

Tool Count5/5

Six tools is well-scoped for a library advisory server, covering selection, verification, documentation, usage, and dependency auditing without redundancy. The count is within the ideal range and each tool serves a distinct purpose.

Completeness5/5

The tool set covers the full lifecycle: pick a library (pick_library), evaluate it (should_i_use), see alternatives (alternatives), get usage snippets (how_do_i), get official docs (docs_link), and audit a project's dependencies (audit_dependencies). There are no obvious dead ends for common library decision workflows.

Resources