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Glama

Fmcsa

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

A4.6/5.0
Behavior5/5

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

Annotations already mark this as read-only, idempotent, and non-destructive, and the description adds rich behavioral context: graceful degradation if GLEIF/OpenFIGI is unavailable, EDGAR identifiers still returning, every identifier being source-labelled, unresolved identifiers being surfaced under `unresolved` rather than omitted, and ownership details included when available. This goes well beyond the annotation hints and is consistent with them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The description is information-dense and front-loaded with use examples and the "Use FIRST" guidance, but the company clause is a massive run-on with nested parentheses and em-dash digressions, making it harder to scan than it should be. Some content also duplicates the schema's value description, and bulleted structure would improve readability.

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 present, the description carries the full burden of explaining return behavior, and it does so thoroughly: it names the output identifiers for company and drug types, describes ownership data, states failure behavior via `unresolved`, and covers enrichment degradation. Given the tool's complexity and two entity types, 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.

Parameters5/5

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

Although the schema covers 100% of parameters, the description extends meaning by adding ISIN as a valid company input, explaining how an ISIN resolves to the legal issuer, and specifying that the tool accepts entity-name-only input for instruments like bonds. It also clarifies what each entity type returns (CIK, ticker, LEI, FIGI, RxCUI, etc.), which is not fully captured in the schema descriptions.

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 states a specific verb and resource: it resolves a user-spoken NAME to the canonical/official identifiers that other tools require as input. It also lists concrete example questions, supported entity types (company and drug), and the exact identifiers returned, which clearly distinguishes it from sibling tools like entity_profile or the fmcsa lookups.

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?

The description explicitly says "Use FIRST whenever you have a name but need an ID," which is a clear when-to-use signal. It also explains that the tool replaces 2-3 manual lookups, but it does not explicitly name alternative tools or state when not to use it beyond the inverse of needing a name rather than an ID.

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

A3.8/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is notable overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, as are bet_research and polymarket_edges. Detailed descriptions help, but an agent could still struggle to pick the right one.

Naming Consistency2/5

Naming conventions are mixed: snake_case (fmcsa_carrier_lookup), verb_noun (ask_pipeworx, recall), and phrases (suggest_questions, generate_llms_txt). No consistent pattern, making it harder for an agent to infer tool purpose from name alone.

Tool Count3/5

35 tools is high for a single server, but the server aggregates many domains. While each tool may serve a purpose, the count feels bloated and beyond typical scope (3-15). Some tools could be merged (e.g., the ask_pipeworx variants).

Completeness2/5

For the FMCSA domain, the four tools provide decent coverage. However, the server includes many tools for other domains (e.g., prediction markets, company profiles) without full lifecycle support (e.g., only lookup, no create/update). The set feels like a random collection rather than a coherent domain.

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