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List High-DR Entity & Knowledge Graph Platforms

list_entity_sources
Read-onlyIdempotent

Retrieve verified DR 70-98 self-serve entity & citation platforms (Wikidata, CERN Zenodo DOI, Hugging Face, GitHub, npm, PyPI, LinkedIn) for Knowledge Graph sameAs synchronization.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_entity_sources"
  }
}

Input Schema

NameRequiredDescriptionDefault
categoryNoOptional filter (e.g. all, academic, developer, social).

Output Schema

NameRequiredDescriptionDefault
countYes
sourcesYes
entity_sourcesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered without the description. The description adds genuine value by signalling a curated, fixed, quality-gated set ('verified DR 70-98 self-serve'), which reinforces the closed-world behavior. It does not discuss result shape or pagination, but an output schema exists to cover that.

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?

A single dense sentence with no wasted clauses, and the resource scope is front-loaded before the use case. The marketing-style qualifiers ('DR 70-98', 'self-serve') add scoping information rather than filler, though the phrasing is somewhat jargon-heavy for an agent reader.

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

Completeness4/5

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

This is a simple, non-destructive list tool with one optional parameter and a published output schema, so return values need no explanation. The description plus annotations give everything needed to call it correctly. Only the absence of explicit sibling routing keeps it below a 5.

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?

With a single optional parameter at 100% schema description coverage, the schema already documents 'category' with example values. The description's platform list loosely hints at the category groupings (academic, developer, social) but supplies no enum values, defaults, or filtering semantics beyond what the schema states. Baseline 3 applies.

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

Purpose4/5

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

The description uses a specific verb ('Retrieve') and a precisely scoped resource: verified DR 70-98 self-serve entity and citation platforms, with concrete examples (Wikidata, Zenodo DOI, Hugging Face, GitHub, npm, PyPI, LinkedIn). That is far more specific than a restated title. It does not explicitly contrast itself with any sibling, so it stops short of a 5.

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

Usage Guidelines3/5

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

Usage is only implied through the stated purpose 'for Knowledge Graph sameAs synchronization', which tells the agent the downstream context but not when to call this versus another tool. There are no exclusions, prerequisites, or named alternatives. Adequate but leaves selection to inference.

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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