FactGrid (Renamed to FactReason)

Package upgrade advisory

factreason_package_upgrade_advisory

Read-onlyIdempotent

Compare two exact published npm or PyPI versions and return publisher-declared registry metadata changes, including yanks, exports, module format, runtime floors, peers, and licences, with before/after evidence. Set responseFormat="compact" for tokenizer-measured context savings; the backward-compatible default is "full". This is a read-only metered lookup; misses are never billed and every API key carries one-off trial credit. Use it for package metadata; use factreason_api_breaking_changes for a third-party HTTP API, and consult changelogs for behavioural changes.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`to`	Yes	Exact target version, e.g. "5.0.0"
`from`	Yes	Exact currently installed version, e.g. "4.1.2"
`name`	Yes	Exact package name, e.g. "chalk" or "urllib3"
`registry`	Yes	Registry containing both package versions
`responseFormat`	No	Use compact to remove repeated prose and fields while retaining evidence; full preserves the legacy response contract	full

Output Schema

TableJSON Schema

Name	Required	Description	Default
`found`	No
`scope`	No
`billed`	No
`message`	No
`toVersion`	No
`advisories`	No
`fromVersion`	No
`packageName`	No
`tokenMetrics`	No
`advisoryCount`	No
`breakingCount`	No

TDQS

A4.9/5.0

Behavior5/5

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

Although annotations already declare read-only/idempotent/non-destructive behavior, the description adds materially useful context: it is a metered lookup, misses are never billed, every API key carries one-off trial credit, and compact mode affects context usage. This goes beyond the annotations without contradicting them.

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 carry the core behavior, billing/response-format context, and alternative tool guidance without waste. The most important purpose is front-loaded, and every sentence earns its place.

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?

The tool is moderately complex, but the description covers the essential invocation context: registry coverage, exact-version comparison, evidence output, billing semantics, response format options, and the main sibling distinction. With a full output schema and comprehensive input schema, no critical guidance appears missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already documents every parameter with examples, enums, defaults, and constraints, so the description only needs limited parameter guidance. It adds meaningful value by explaining that responseFormat='compact' provides tokenizer-measured context savings and that 'full' is backward-compatible.

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 names a specific verb, resource, and scope: compare two exact published npm or PyPI versions and return publisher-declared registry metadata changes. It also distinguishes itself from the sibling tool factreason_api_breaking_changes by clarifying that the sibling is for third-party HTTP APIs.

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?

It provides explicit when-to-use and when-not-to-use guidance: use for package metadata, use factreason_api_breaking_changes for third-party HTTP APIs, and consult changelogs for behavioral changes. It also states the responseFormat switching context.

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

A4.3/5.0

Disambiguation4/5

Most tools clearly target distinct tasks, and the descriptions cross-reference each other (e.g., package_upgrade_advisory vs api_breaking_changes, api_schema vs integration_brief). However, a few tools overlap in conceptual space—especially factreason_api_schema, factreason_auth_playbook, and factreason_integration_brief—so an agent may need to read the descriptions carefully.

Naming Consistency3/5

All names share the factreason_ prefix and snake_case, but the naming pattern is mixed: some are noun phrases (api_schema, auth_playbook, deprecation_scan) while others use imperative verbs (create_key, discover_api, subscribe_spec_changes). The names are readable and not chaotic, but they do not follow a single predictable convention.

Tool Count5/5

Eleven tools is well within the appropriate range, and the count fits the broad API-intelligence scope. Each tool seems to earn its place by covering a distinct phase of researching, comparing, or subscribing to API and package metadata.

Completeness3/5

The set covers discovery, schemas, auth, breaking changes, deprecation, package upgrades, and component spec lookup, which is a reasonably deep surface. However, the subscription feature creates watches but provides no tool to list, cancel, or delete them, and there is no balance/key management tool beyond key creation and top-up links.

Package upgrade advisory

Input Schema

Output Schema

TDQS

Glama MCP Gateway

TDQS

Resources