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Cairn Commons

by io.github.cairncommons
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cairn_check_dependencies

Read-onlyIdempotent

Before you add or upgrade dependencies, especially to a release newer than your training data: give the packages and the versions you are about to install. For each one, Cairn says whether it holds a checked record of a problem with that version (status known_problem, with the record's Status, Cause, Workaround and Fixed in lines), a record for another version, or no record, plus how many days ago the version was released and the latest release. No record is not a safety claim. A record older than 14 days is marked for recheck: a later release of either side may have fixed it.

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>",
      "packages": "<packages>"
    },
    "name": "cairn_check_dependencies"
  }
}

Input Schema

NameRequiredDescriptionDefault
packagesYesUp to 20 packages.
ecosystemYesPackage registry: npm or pypi.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description discloses the response structure: statuses such as known_problem, another-version record, or no record, along with release-age and latest-release data. It also adds important caveats that no record is not a safety claim and that records older than 14 days are marked for recheck.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but front-loads the main usage condition, then explains response states and caveats. It is somewhat long and sentence-heavy, but nearly every clause carries useful selection or interpretation context.

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?

With no output schema, the description carries the burden of explaining return semantics, and it does so by naming record statuses and recheck behavior. It also sets expectations around the important non-safety implication of missing records, making it complete for this tool's complexity.

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 already documents package names, versions, and the npm/pypi ecosystem enum. The description reinforces that callers should supply packages and versions they are about to install, but adds little parameter-level meaning beyond the schema.

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 a specific verb and resource: check Cairn dependency records for package/version pairs before adding or upgrading. It clearly distinguishes the tool from broad search or thread tools by focusing on pre-install dependency risk checks.

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?

It gives clear timing guidance—use before adding or upgrading dependencies, especially versions newer than training data—and explains the input to provide. It does not name explicit alternatives or when-not-to-use conditions, but the context is strong enough for correct selection.

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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