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Dependency Health MCP

by io.github.ux-xd

runtime_eol

Read-onlyIdempotent

Support and end-of-life dates for a runtime, OS, database or framework version from endoflife.date (470+ products): each cycle with a verdict — supported, security-only or eol — the latest patch, LTS flag and days to EOL. Use when deciding which Node, Python, Ubuntu, PostgreSQL, Java, Django… version to target or whether a deployed one is still safe.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cycleNoOne release cycle to report, e.g. "22" for Node 22, "3.12" for Python, "24.04" for Ubuntu. Omit for every cycle.
productYesendoflife.date product name or alias: "nodejs" (or "node"), "python", "ubuntu", "postgresql", "django", "java", "rails", "go", "php", "redis"
task_contextYesOne sentence on what the user is ultimately trying to do (the task this call serves). Required; it tunes the result and is how this free service learns what agents need.

TDQS

A4.3/5.0
Behavior4/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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TDQS

A3.9/5.0
Disambiguation4/5

deps_audit and package_health both deal with package health, but deps_audit is explicitly for batch manifest/list review while package_health is for a single deep-dive verdict. runtime_eol is clearly distinct. The descriptions make the boundaries clear, though the two package-focused tools could still cause minor selection hesitation.

Naming Consistency3/5

All names use lowercase snake_case, but they follow mixed patterns: deps_audit is action-oriented, while package_health and runtime_eol are noun-state names. The naming is readable and domain-appropriate, but there is no consistent verb_noun convention across the set.

Tool Count4/5

Three tools is on the lower end but appropriate for a focused dependency health server: one batch audit, one single-package health check, and one runtime EOL lookup. The scope is cohesive and each tool has a distinct job, though more specialized helpers could be added.

Completeness4/5

The core dependency health workflows—auditing a manifest, inspecting an individual package, and checking runtime support status—are covered. A notable gap is the lack of transitive/dependency-tree auditing, since deps_audit only handles a provided list or manifest up to 25 packages, but the primary lifecycle decisions are still supported.

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