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by io.github.presendapp
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Package typosquat check

typosquat_check
Read-onlyIdempotent

Call before installing a package whose name you typed or recalled. Checks whether an npm or PyPI package name is a near-miss of a well-known package (typosquatting), with an edit-distance threshold scaled to name length; names of 3 characters or fewer are not fuzzy-matched. Uses a curated list of popular names, so a clean result does not prove a package is safe. It does not check that the package exists: supply_chain_check does.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ecosystem": "<ecosystem>",
      "package": "<package>"
    },
    "name": "typosquat_check"
  }
}

Input Schema

NameRequiredDescriptionDefault
packageYesPackage name to check for likely typosquatting of a well-known package in the given ecosystem.
ecosystemYesPackage ecosystem, e.g. npm or PyPI.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / ecosystem / description
      Previous value: -""New value: +"Package ecosystem, e.g. npm or PyPI."
    • changedInput schema / properties / package / description
      Previous value: -""New value: +"Package name to check for likely typosquatting of a well-known package in the given ecosystem."
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description goes well beyond them: it discloses the edit-distance threshold scales with name length, that names of 3 characters or fewer are not fuzzy-matched, that matching is against a curated popular-name list, and the important caveat that a clean result does not prove safety. These are exactly the behavioral limits an agent needs to interpret a negative result correctly.

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 short sentences, action guidance front-loaded, then mechanism, then limitations. Every sentence carries distinct information with no repetition of the name or title.

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?

No output schema exists, so the description should ideally say what a positive/negative result looks like (matched name, distance, confidence). It covers meaning of a clean result and the non-existence gap, making it nearly complete, but the return shape is left implicit.

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 coverage is 100%, so the schema already documents both parameters, and the description adds no new per-parameter detail (format, casing, validation rules). The phrase 'whose name you typed or recalled' frames intent but does not enrich the parameter semantics. Baseline 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?

States a specific verb and resource ('checks whether an npm or PyPI package name is a near-miss of a well-known package (typosquatting)') and explicitly contrasts itself with the sibling supply_chain_check. An agent can distinguish it from the four other check tools without opening any schema.

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?

Gives an explicit trigger ('Call before installing a package whose name you typed or recalled') plus an explicit exclusion and alternative ('It does not check that the package exists: supply_chain_check does'). This is textbook when-to-use / when-not-to-use routing.

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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