Dependency Health MCP

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is covered. The description adds useful context beyond annotations: the ≤25-package batching limit, the per-package verdict categories, and the summary output. It does not discuss output ordering, error cases, or how the task_context tunes results, but that is acceptable given 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.

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

The description is two sentences with no wasted words. The main deliverable is front-loaded, the package limit is placed parenthetically, and the use case is stated at the end without repetition of schema details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description carries the responsibility of conveying return expectations; it does so well by enumerating the verdicts, ages, advisories, replacements, and summary. It also names the manifest formats and the use context. It could be slightly richer about how to interpret the verdict levels, but overall an agent has enough context to invoke it correctly.

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

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

Schema description coverage is 75%, so the schema already documents most parameters meaningfully. The description adds light semantic glue by tying 'manifest' and 'list' to the manifest/packages parameters, but it does not meaningfully enrich parameter understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear purpose: producing a per-package health verdict from a manifest or list, with specific health categories and version/age/advisory details. It implies bulk analysis ('every package... in one call') which helps separate it from the single-package sibling package_health, though it never names that sibling directly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says when to use the tool ('Use when reviewing a repository's package.json or requirements.txt before an upgrade, an audit or a fork decision'). It does not mention when not to use it or name alternatives such as package_health or runtime_eol, so it misses the 'when-not' guidance needed for a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds meaningful detail beyond those annotations by listing possible verdicts and the concrete evidence categories returned, such as per-version deprecation, yanked/retracted releases, repository push dates, maintainer/dependent counts, and advisory IDs. It does not cover failure modes or response structure, but the added context is substantive.

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

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

The description is dense but efficient: the opening question frames the verdict, the evidence list is a compact enumeration, and the final sentence gives the invocation context. There is no filler or repetition of schema fields, and the most important information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Since there is no output schema, the description appropriately carries the return-value burden by naming the verdict categories and the exact evidence an agent should expect. It leaves some details unspecified, such as response structure and error handling, but for a read-only health-check tool the provided information is sufficient for an agent to predict behavior.

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

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

Schema description coverage is 100%, with each parameter already documented, including the version parameter's note that omitting it checks the latest. The tool description adds little new parameter-level meaning: it references the exact version, but this is already captured in the schema. A baseline of 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a concrete question and states exactly what the tool returns: a single verdict among deprecated/archived/stale/active/unknown plus a rich evidence list. This clearly identifies the resource and outcome, though it does not explicitly contrast itself with sibling tools like deps_audit or runtime_eol.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use: 'Use when choosing, pinning or upgrading a dependency' across eight named ecosystems. This is a clear and actionable trigger, but it does not mention when not to use this tool or name alternative sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already establish readOnly/openWorld/idempotent safety. The description adds useful behavioral context: data comes from an external service (endoflife.date), each cycle includes verdict/patch/LTS/days-to-EOL detail, and task_context tunes results and serves as telemetry for the service. No contradiction with annotations.

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

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

Two sentences deliver rich, non-redundant information: the first covers what the tool returns and where the data comes from; the second gives a concrete use-case. No filler or repeated schema content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description enumerates the return payload (verdict, latest patch, LTS flag, days to EOL), states the data source, and gives a context for invocation. Combined with the well-described schema, an agent has enough to select and call it correctly.

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

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

Schema coverage is 100%, so the structured schema already documents product, cycle, and task_context. The description reinforces the product categories and what outputs cycles contain, but adds no parameter-specific semantics beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the resource: support and end-of-life dates for runtimes, OSes, databases, and frameworks from endoflife.date. It differentiates itself through scope (470+ products, verdicts like supported/security-only/eol) and references sibling-relevant domains like Node, Python, Ubuntu, PostgreSQL, Java, Django.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit 'Use when…' guideline for deciding version targets or checking deployed safety. It does not mention exclusions or directly name sibling tools as alternatives, so it stops short of full when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.