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Glama

Gov Bids

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 cover readOnly/idempotent/non-destructive; the description adds genuinely useful behavior: graceful degradation when GLEIF/OpenFIGI are down, explicit `unresolved` entries, internal cascading through multiple lookups, and coverage of non-equity instruments. No contradiction with annotations.

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?

Front-loaded with examples and the 'Use FIRST' rule, but the supporting details run together in long semicolon chains and parenthetical asides, making the middle harder to scan. Dense but not well structured.

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?

Despite having no output schema, the description tells the agent what will be returned for each type, what input formats are accepted, how unresolved identifiers are reported, and how failures degrade. For a two-parameter resolution tool, this is sufficient to call correctly.

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?

The description significantly enriches the schema: it explains what ticker/CIK/ISIN/name inputs produce, when ISIN-to-LEI mapping applies, and what 'company' vs 'drug' return. The schema's value description is already strong, and the narrative adds source-level semantics 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?

The opening frames the tool around concrete user phrasings and a specific job: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' This clearly identifies the resource (entity name → canonical ID) and separates it from sibling profile/compare/search tools.

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 trigger: 'Use FIRST whenever you have a name but need an ID,' and enumerates accepted input forms across supported types. It does not name alternatives or list when not to use it, though the first-use rule strongly implies the choice.

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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the multiple Pipeworx query variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and multiple Polymarket tools could cause some confusion. However, descriptions are detailed enough to differentiate them.

Naming Consistency3/5

Naming patterns are mixed: some tools use verb_noun (list_subscriptions, open_bids_search), others use noun_phrase (entity_profile, bet_research), and cases are inconsistent (snake_case vs underscores). While not chaotic, the lack of a strong consistent pattern reduces coherence.

Tool Count2/5

33 tools is high, and the server's name 'Gov Bids' suggests a focused scope, but most tools are unrelated (AI visibility, npm packages, general data queries). The tool count feels excessive for a focused server, and the scope is too broad.

Completeness4/5

For a general-purpose data server, the tool set is quite comprehensive across multiple domains (SEC, FDA, economics, prediction markets, etc.). Minor gaps exist (e.g., government contracts beyond bids), but overall coverage is strong.

Resources