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maminihds

attester-mcp

by maminihds

Package Exists (free, keyless)

attester_package_exists
Read-onlyIdempotent

Check whether a package name exists on PyPI or npm, and flag typosquatting alternatives within edit distance 2, preventing installation of nonexistent or mistyped packages.

Instructions

WHAT: checks whether a package name really exists in PyPI or npm. On a miss, typosquat_adjacent flags real names within edit distance 2. WHEN: before installing or importing a package a model suggested; published studies measure 5-20% hallucination rates for suggested package names. WHEN NOT: for private packages outside the public registries. COST: free (25 calls/day per client IP shared across the four attester_* demo tools, reset 00:00 UTC; over quota the response points at the paid route). Paid route /v1/package/exists is $0.002 per call. OUTPUT: {exists, latest_version, typosquat_adjacent, adjacent_to, proof: {artifact_sha256, source_url}, attestation, attestation_hash, signature, served_in_ms}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPackage name (e.g. "requests", "lodash").
ecosystemYes"pypi" or "npm".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description goes well beyond by disclosing cost limits (25 calls/day, shared quota, reset time), a paid fallback route, and the exact output shape including proof/attestation fields. This adds real decision-making context beyond the annotations.

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?

Structured with WHAT/WHEN/WHEN NOT/COST/OUTPUT, front-loaded with the core purpose. Every sentence adds distinct information (cost, quota, typosquat, output). No fluff or redundancy, despite length.

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?

Tool complexity is moderate due to rate limits, paid route, and typosquat behavior. The description covers all of these, gives the full output shape (also in output schema), and includes cost details. Combined with strong annotations, the description is complete for selecting and invoking this 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% for both parameters (name and ecosystem) with examples. The description reinforces these but adds little new syntactic detail—its main additions (typosquat_adjacent, cost) relate to output and usage rather than parameter semantics. Baseline 3 applies since schema carries the load.

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 opens with a clear WHAT: 'checks whether a package name really exists in PyPI or npm.' This is a specific verb+resource (packages in public registries) and inherently distinguishes from sibling tools like attester_symbol_exists. It adds the typosquat behavior, further clarifying its unique role.

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?

Explicit WHEN: 'before installing or importing a package a model suggested' with supporting evidence (5-20% hallucination rates). Explicit WHEN NOT: 'for private packages outside the public registries.' This gives the agent clear conditions for use and exclusion.

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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