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Glama

gutendex

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. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark it read-only/open-world/idempotent, and the description layers on substantial behavioral detail: graceful degradation when GLEIF/OpenFIGI are down, internal cascading of lookups, ambiguity handling via figi_candidates, explicit unresolved fields, source labelling, and ISIN-to-LEI resolution. This far exceeds what annotations alone provide.

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, but nearly every clause carries operational value and it is front-loaded with the usage rule. However, it is a single monolithic block with heavy parentheticals; structured bullets would improve scannability at little cost.

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 2-parameter resolver with no output schema, it covers supported types, accepted inputs, resolution behavior, failure modes, and return-side concepts (figi_candidates, unresolved, source labels). There are enough behavioral details for an agent to invoke it correctly and interpret results.

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?

While schema coverage is 100%, the description adds crucial semantics beyond the schema: detailed examples for both type values, the ENTITY NAME ONLY rule, bond issuer formatting warnings, and accepted input forms (ticker, CIK, ISIN, brand/generic name). It materially improves correct invocation.

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-phrasing examples and states the core operation: resolving a spoken name to canonical identifiers that other tools require. It distinguishes itself from sibling lookup/profile tools by positioning it as the first-stop name-to-ID resolver.

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 gives an explicit condition: 'Use FIRST whenever you have a name but need an ID.' It covers company and drug types and clarifies input formats. It doesn't explicitly name sibling alternatives or state when to stop using resolve_entity and switch to entity_profile or compare_entities, so it lacks full exclusion 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

A3.6/5.0
Disambiguation2/5

Multiple tool clusters are nearly indistinguishable: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded overlap heavily (beta is explicitly identical right now), and five polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) all concern prediction-market edge detection with fuzzy boundaries. entity_profile, recent_changes, and compare_entities also blur together for company research. Only the book tools are cleanly distinct, but they are drowned by the surrounding ambiguity.

Naming Consistency4/5

Most tools follow a consistent snake_case pattern and generally lead with a verb or clear noun (search_books, get_book, subscribe, unsubscribe, validate_claim, resolve_entity). A few depart from the verb-first convention (entity_profile, bet_research, pipeworx_trending, polymarket_edges) but the deviations are minor and do not hinder readability.

Tool Count2/5

35 tools is already heavy, but the critical problem is scope: the server is named gutendex (a book API) yet only 4 of 35 tools relate to books, with the other 31 forming an unrelated Pipeworx data/research/prediction-market suite. The count is inappropriate for the advertised purpose — it feels like two or three separate servers crammed into one.

Completeness2/5

For the gutendex domain, the book tools are thin: search, get, popular, and topic browsing exist, but common Gutendex capabilities like author browsing, language filtering, sorting, and pagination controls are missing. For the actual Pipeworx suite the surface is broad, but the server's stated purpose is gutendex, and the overwhelming majority of tools are completely off-topic, creating a severe coverage mismatch.

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