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Glama

Opencitations

by io.github.pipeworx-io

Resolve Entity

resolve_entity
Read-onlyIdempotent

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns figi_candidates to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under unresolved rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valueYesFor company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed ("NEW YORK ST DORM AUTH"), never the question's full noun phrase ("NEW YORK ST DORM AUTH revenue bonds"): the FIGI lookup matches instrument names, so trailing security-class words match nothing.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / value / description
      Previous value: -"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed (\"NEW YORK ST DORM AUTH\"), never the question's full noun phrase (\"NEW YORK ST DORM AUTH revenue bonds\"): the FIGI lookup matches instrument names, so trailing security-class words match nothing."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The annotations already mark it read-only, open-world, and idempotent, and the description adds significant behavioral context: multi-source cascading lookups, graceful degradation when GLEIF or OpenFIGI is unavailable, explicit `unresolved` reporting, and ambiguous matches returning `figi_candidates`. This is much richer than the 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long and dense with many parenthetical asides, but it is front-loaded with usage examples and organized by type. Every sentence conveys functional information, so the length is mostly justified by the tool's complexity.

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?

With no output schema, the description compensates by naming the returned identifiers (CIK, ticker, LEI, FIGI, RxCUI), the `unresolved` field, `figi_candidates` behavior, and source labeling. It also covers edge cases such as non-equity instruments, ISIN-to-LEI mapping, and partial enrichment failures, so an agent has nearly everything needed to select and invoke it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the schema already documents both parameters fully, the description adds essential semantics: accepted input forms for company and drug, the ISIN-to-LEI behavior, and a precise warning to pass only the legal entity name as printed rather than the full noun phrase. This materially helps an agent call the tool correctly.

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 concrete user-spoken queries and states a clear verb-resource pair: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It also enumerates supported entity types and the exact identifiers returned, making it easy to distinguish from sibling tools that are not name-to-ID resolvers.

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 explicitly instructs 'Use FIRST whenever you have a name but need an ID,' and gives context that it replaces 2-3 manual lookups. It does not explicitly name an alternative tool or state when not to use it, but the supported types and accepted input forms provide strong situational 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

A3.7/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and citation vs citations differ only by pluralization while actually accepting different ID types (OCI vs DOI). The server is named Opencitations but 31 of 37 tools serve unrelated purposes, so an agent cannot infer what the set is for without reading very long descriptions.

Naming Consistency3/5

All names are snake_case and family prefixes (polymarket_*, ask_pipeworx_*, pipeworx_*) give some predictability. However, conventions are mixed: bare resource nouns (citation, citations, references, metadata) coexist with verb_noun tools (resolve_entity, validate_claim) and noun phrases (recent_changes, entity_profile, deep_research), so there is no single pattern that lets an agent predict tool names.

Tool Count2/5

37 tools is well past the 25+ threshold for a heavy set, and most are redundant with the server's own router tools — ask_pipeworx already reaches 5,759 underlying tools, making many direct tools overlapping conveniences. The scope also wildly overshoots the server name: only 6 of 37 tools serve the Opencitations citation-graph domain.

Completeness3/5

For the named OpenCitations domain, the core citation-graph read operations exist (metadata, incoming citations, outgoing references, counts, OCI record lookup), but there is no paper/DOI discovery or search tool — an agent must already possess a DOI, a dead end for title/author queries. The broader accidental domain (data routing, company research, prediction markets, monitoring) is covered unusually well, but that does not serve the server's stated purpose.

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