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cern-inspire-mcp-server

by io.github.cyanheads
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INSPIRE reference vocabulary

cern_inspire_list_reference
Read-onlyIdempotent

Decode the vocabulary the other cern_inspire tools take: INSPIRE search syntax (field operators, boolean logic, sort orders, the 10,000-result window, and how malformed queries behave), identifier forms (recid, arXiv, DOI, BAI, ORCID, INSPIRE ID, institution recid, texkey, HEPData DOIs), the document_types and subjects filter values, citation-summary buckets, and HEPData record versions and DOIs. Static content with no upstream call; use it to build a query or to recover from an empty or unexpectedly broad result.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "topic": "<topic>"
    },
    "name": "cern_inspire_list_reference"
  }
}

Input Schema

NameRequiredDescriptionDefault
topicYesWhich vocabulary to decode: search_syntax (query operators and rules), identifiers (paper, author, experiment, institution, and HEPData identifier forms), document_types, subjects, citation_buckets (citation-summary ranges and terms), or hepdata (record versions, DOIs, and licence).

Output Schema

NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
topicNoThe topic decoded.
entriesNoThe entries for the topic.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/not-open-world, so the safety profile is covered. The description adds genuinely useful behavior beyond that: 'Static content with no upstream call' (no network, always succeeds) and the fact that it documents the 10,000-result window and how malformed queries behave. It stops short of describing the response shape, but that is minor given the output schema exists.

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?

Front-loaded with the core purpose, then the enumerated topics, then the usage trigger. The single long sentence listing topics is dense but every clause maps to an actual enum value, so little is wasted; it could be marginally tighter.

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 one-enum static reference tool with an output schema already present, the description covers everything an agent needs: what each topic contains, that it is offline/static, and when to reach for it. No return-value explanation is required.

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 description coverage is 100% and the single enum parameter is fully documented in the schema, so the baseline is 3. The description restates the enum categories (search syntax, identifier forms, etc.) with marginally more detail than the schema, but adds no format or syntax guidance beyond it.

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?

States a specific verb+resource: 'Decode the vocabulary the other cern_inspire tools take,' and then enumerates exactly which vocabularies (search syntax, identifier forms, document_types, subjects, citation buckets, HEPData). This clearly distinguishes it from the search/get siblings, which consume that vocabulary rather than explain it.

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?

Gives explicit trigger conditions: 'use it to build a query or to recover from an empty or unexpectedly broad result.' It also clarifies the relationship to the alternatives ('the other cern_inspire tools take'), so an agent knows this is the pre-query/recovery helper rather than a retrieval tool.

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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