cern-inspire-mcp-server

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

Annotations already declare readOnly, idempotent, and openWorld, so the safety profile is covered. The description adds genuinely useful behavioral detail beyond that: the 50-entry page size, the 10,000-match reachability ceiling, and the fact that entries are INSPIRE's verbatim text with abbreviated long author lists. Only minor gaps remain (e.g., no note on output shape beyond the existing output schema).

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?

Front-loaded with the core action and output formats, then usage modes, then pagination caveats. Dense but every sentence carries information; slightly long, with the author-abbreviation note being a nice-to-have rather than essential.

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?

An output schema exists, so return values need not be restated. Combined with the pagination ceiling, format options, and query routing, the description gives an agent everything needed to invoke the tool correctly against its complex query semantics.

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 baseline is 3. The description still adds value by explaining how the query parameter maps to use cases (named papers vs. topic/author/citing set) and what the format options produce ('\bibitem entries in European or US style'), which goes beyond the schema's terse enum labels.

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?

States a specific verb+resource (export INSPIRE-HEP citation entries for papers a query selects) and the output forms (BibTeX / LaTeX \bibitem, EU or US style). It is clearly distinguishable from siblings like get_citation_summary or search_literature, which retrieve or search rather than emit formatted bibliography entries.

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?

Explicitly covers the two main usage modes: naming specific papers via recid/arxiv/doi, or exporting a topic, author, or citing set using any query cern_inspire_search_literature accepts. It also routes the agent to cern_inspire_list_reference topic search_syntax for query grammar, which is exactly the alternative-selection guidance needed.

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 cover the safety profile (readOnly/idempotent/openWorld), and the description adds real behavioral context the annotations do not: that any of the year/self-citation filters removes citations-per-year from the output because INSPIRE cannot narrow it that way. It stops short of describing performance or rate limits, so not a 5.

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 metrics are front-loaded before the parameter guidance, and each sentence carries substantive information. It is dense and on the long side, but there is little pure filler; only the inline enumeration of citation buckets slightly duplicates the output schema's job.

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?

For a 7-parameter, zero-required tool with an output schema, the description covers input alternatives, prerequisite resolution, filter interactions, and the mutable-output caveat. An agent has everything needed to select and invoke it correctly, with no gaps in the description's own responsibilities.

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%, which already sets a strong baseline, but the description goes further by explaining what counts as valid author identifiers (BAI/ORCID/INSPIRE ID/recid, not a name) and that query accepts topics, collaborations, institutions, or 'a <BAI>'. It adds meaning beyond the schema rather than restating it.

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?

States a specific verb and resource ('Compute INSPIRE-HEP's citation summary') and enumerates the exact metrics produced (h-index, citation totals, per-bucket counts, citations per year), which lets an agent distinguish it from siblings like cern_inspire_export_citations or cern_inspire_search_literature.

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?

Gives an explicit constraint ('Pass exactly one of author ... or query'), a prerequisite ('resolve a name with cern_inspire_search_authors first'), and describes how the optional filters narrow the result. It routes the agent to the sibling needed before calling this tool.

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, idempotent and openWorld semantics, so the bar is lower; the description adds real behavioral context by disclosing the heavy return payload and the max_authors/authorCount truncation behavior for large collaboration papers. It omits auth requirements and rate limits, so it is not fully rich.

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?

Front-loads the verb and identifier forms, and the trailing note about author capping is well placed. However, the long mid-sentence catalogue of returned fields is verbose and partly redundant given an output schema exists.

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?

For a bounded two-parameter lookup with a full output schema and covering annotations, the description supplies everything an agent needs: what is fetched, from which identifiers, and how author lists are limited. No material gap remains.

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%, both parameters are fully documented there, so baseline 3 applies. The description's note that max_authors caps the list while authorCount holds the full count largely restates the schema wording rather than adding new meaning.

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?

States a specific verb and resource ('Fetch one paper's full INSPIRE-HEP record') and enumerates the identifier forms accepted, which clearly separates it from the sibling search_literature tool that would be used to find papers rather than retrieve one.

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?

Gives clear context that this is a single-record lookup keyed on recid/arXiv ID/DOI, and enumerates the accepted identifier syntaxes in the schema. It never explicitly names an alternative (e.g. 'use search_literature when you only have a title') or states exclusions, so it stops short of 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 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.

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.

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.

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.

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.

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.

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

Annotations already cover safety (readOnly, idempotent, openWorld), so the description's job is to add match behavior — and it does: names return ranked candidates with ambiguity risk, identifiers match exactly. It stops short of describing pagination or result ordering beyond ranking, but the added ambiguity warning is genuinely useful.

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?

Front-loaded with the purpose, then value, then usage guidance; every sentence earns its place. It is dense and slightly long, but not padded — no restatement of the title or name.

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?

With an output schema present, the description needn't enumerate returns, yet it still previews the profile shape and the literatureQuery handoff. Together with the routing advice, an agent has everything needed to 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% and the schema already lists accepted identifier formats, so baseline is 3. The description adds match semantics per query type (ranked candidate list for names vs exact for identifiers) that the schema does not convey, pushing it slightly above baseline.

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?

States a specific verb and resource (find physicist profiles in INSPIRE-HEP) and enumerates the five accepted lookup keys. It is clearly distinguishable from siblings like cern_inspire_search_literature and cern_inspire_get_citation_summary, which it names.

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?

Explicitly routes the agent: confirm the person via positions/categories before passing a bai or recid to cern_inspire_get_citation_summary, and notes the literatureQuery feeds cern_inspire_search_literature. It also warns that a bare surname can match tens of thousands of profiles and that a less-cited namesake can rank first, which is real when-not-to-trust guidance.

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 declare readOnly, idempotent, and openWorld, so the safety profile is covered. The description adds real behavioral value beyond that: the exact record contents (accelerator, institutions, subgroups, lifecycle dates, paper count) and the chaining affordance via literatureQuery, which an agent could not infer from annotations alone.

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, front-loaded with the searchable resource and examples before the record-shape detail. Dense but every clause carries information; slightly long, though no sentence is wasted.

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?

An output schema exists, so return values need not be re-explained, yet the description's enumeration of record fields is still useful for judging downstream chaining to the literature tools. Combined with annotations covering safety, nothing essential for correct invocation is missing.

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%, and the schema description already spells out the accepted query forms (name, legacy name like CERN-LHC-CMS, digits-only recid). The description restates these acceptable inputs but adds no format details or edge-case semantics beyond the schema, so the baseline 3 applies.

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?

States a specific verb ('Find') and resource ('experiments, collaborations, and facilities in INSPIRE-HEP') with concrete examples (ATLAS, CMS, DUNE, Belle II). The scope is clearly narrower than search_literature or search_authors, so an agent can select it without opening any schema.

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?

Gives clear context on what can be searched (name, accelerator, INSPIRE legacy name, recid) and explicitly routes downstream work to cern_inspire_search_literature or cern_inspire_get_citation_summary via the literatureQuery field. It stops short of stating when to prefer this over search_authors or search_hepdata, so it is not a full when/when-not statement.

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?

Goes well beyond the read-only/idempotent annotations: it enumerates the payload fields an agent will get back (recids, collaborations, keywords, recordDoi, latest version, table count, hepdataUrl), states explicitly that the table values themselves are NOT returned, and discloses the 10,000-result reachability cap. These are exactly the traits an agent needs to plan a call and a follow-up.

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 purpose and the key routing hint are front-loaded, and the payload enumeration is information-dense rather than padding. The first sentence is long and clause-heavy, but every clause carries a distinct fact; only the trailing result-cap sentence is a slight add-on.

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?

The definition covers purpose, selection condition, payload contents, the explicit non-return of data values, and the hard result ceiling, with downstream routing to cern_inspire_get_paper. For a 4-parameter search tool with a full output schema, nothing an agent needs in order to call it correctly is missing.

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 baseline is 3, but the description adds meaning by naming the physics facets the query field operates over (process, observable, energy, collaboration) and by flagging that the result cap applies to the query, which reinforces the page/size limits. It does not add syntax beyond the schema's examples, so it is a modest lift above baseline.

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?

States a specific verb and resource ('Find HEPData measurement records') plus the searchable dimensions (process, observable, energy, collaboration) and the data source (INSPIRE-HEP's index of HEPData submissions). This clearly distinguishes it from sibling paper-oriented tools like cern_inspire_search_literature and cern_inspire_get_paper.

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?

'when the paper is unknown' gives an explicit selection condition against the sibling tool that finds papers, and 'pass to cern_inspire_get_paper' describes the onward step. It stops short of an explicit when-not clause (e.g. 'if you already have the recid or DOI, use X instead'), so it is clear context rather than full routing.

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 cover safety (readOnly, idempotent, openWorld), but the description adds non-obvious behavior: only the first 10,000 results are reachable, and INSPIRE never rejects malformed syntax so a broad/empty result signals a query bug. That is exactly the kind of failure-mode context annotations cannot express.

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?

Front-loaded with purpose and filters, then return fields, routing, and caveats. Every sentence is useful, though the return-field list and malformed-syntax warning partially duplicate the schema and could be tightened.

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?

Given 8 parameters, an output schema, and rich annotations, the description still supplies the return shape, result cap, sibling routing, and query-syntax pitfall. Nothing an agent needs to call this correctly is missing.

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% and the query parameter already documents the INSPIRE operators, so the schema does the heavy lifting. The description's mention of document type/subject/year filtering and sorting mostly restates structured fields rather than adding syntax beyond them.

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?

States a specific verb+resource ('Search INSPIRE-HEP papers') plus scope: query syntax, filters, sort order. It is clearly distinguishable from siblings like cern_inspire_search_authors or cern_inspire_search_experiments without opening any schema.

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?

Explicitly routes the agent: pass a recid to cern_inspire_get_paper for the full record or to cern_inspire_export_citations for BibTeX, and points to cern_inspire_list_reference topic search_syntax when queries misbehave. This is concrete when-to-use-which-tool guidance.

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