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EOSL.ai — Hardware End-of-Life Database

bulk_check

Check up to 200 part numbers in one call (read-only, no auth; for a single part use lookup_part). Returns a per-part row (status, EOSL date, source URL, page URL) plus summary counts: past, endingSoon, supported, active, notFound. Never partial and never destructive: unknown or malformed entries do not fail the call, each simply returns found:false in its row.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
part_numbersYesPart numbers to check; max 200 after case-insensitive deduplication. Punctuation differences (dashes, dots, spaces) are tolerated by the matcher, so raw inventory strings are fine.

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It covers read-only status, auth requirements, non-destructiveness, and failure semantics ('unknown or malformed entries do not fail the call, each simply returns found:false'). This is exemplary for a tool lacking structured 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?

The description is two sentences, front-loaded with the primary function and constraints, then providing output shape and safety guarantees. Every clause adds value with no filler or repetition.

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?

For a tool with one parameter and no output schema, the description thoroughly covers input limits, per-row output fields, summary counts, error behavior, auth, and safety. It leaves no significant gaps for an agent to invoke the tool 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%, and the parameter description already explains maxItems, deduplication, and punctuation tolerance. The tool description adds the context of 'up to 200 part numbers' but does not materially enhance the parameter semantics beyond what the schema provides, so the baseline of 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 opens with a specific verb and resource: 'Check up to 200 part numbers in one call.' It immediately distinguishes itself from the single-part sibling by explicitly directing users to lookup_part for one part. This clearly identifies the tool's scope and unique role.

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?

Explicit usage context is provided: it is for bulk checking, is read-only with no auth, and the description states 'for a single part use lookup_part,' naming the alternative. This gives the agent clear guidance on when to select this tool over its siblings.

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

A4.8/5.0
Disambiguation5/5

Each tool serves a distinct purpose: bulk_check for batch part lookups, lookup_part for single parts, search_models for discovery by name, get_family for full family details, and list_vendors for vendor enumeration. No two tools overlap in their primary use case, and cross-references in descriptions further clarify boundaries.

Naming Consistency4/5

Four of five tools follow a clear verb_noun pattern (get_family, list_vendors, lookup_part, search_models). bulk_check deviates slightly as a compound modifier + noun, but all names are lowercase snake_case and semantically intuitive, making the set predictable overall.

Tool Count5/5

Five tools is ideal for a focused read-only database. The set covers discovery, single lookup, batch lookup, and deep record retrieval without unnecessary redundancy or bloat.

Completeness5/5

The tool surface fully covers the domain of a hardware EOL database: list_vendors confirms coverage, search_models finds families, lookup_part and bulk_check handle part lookups, and get_family provides comprehensive lifecycle data. No obvious operations are missing for the stated read-only purpose.

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