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Glama

Usaspending

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, but the description goes well beyond them. It discloses graceful degradation when GLEIF/OpenFIGI are unavailable, explicit labeling of unresolved identifiers, the 'asserts nothing' ambiguity behavior, cross-source enrichment details, and the fact that one call replaces 2-3 manual lookups. This gives an agent a reliable mental model of side effects and failure modes.

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 dense and front-loaded with example queries and the critical 'Use FIRST' instruction. It is quite long, with many nested clauses and parentheticals, but nearly every sentence carries useful behavioral or semantic content. It earns a high score for structure, though it is slightly more verbose than necessary.

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?

There is no output schema, so the description carries the full burden of explaining return behavior. It explains the shape of results: source-labelled identifiers, explicit unresolved lists, figi_candidates for ambiguous cases, RxCUI/ingredient/brand for drugs, and degradation behavior. Given the tool's high complexity and multiple sources, the description is complete enough for an agent to know what to expect and how to handle failures.

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 well, the description adds substantial meaning beyond the schema: it explains what values are valid for company resolution (ticker, CIK, ISIN, or company name), clarifies that drug accepts brand or generic names, and warns that full noun phrases break FIGI matching. This materially improves the agent's ability to construct correct calls.

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 names a specific action (resolve user-spoken names to canonical identifiers), the target resource (identifiers such as ticker, CIK, LEI, RxCUI), and explicitly differentiates itself from other tools by stating it supplies the IDs other tools require. It also distinguishes supported entity types and gives concrete example queries, so an agent can clearly tell this apart from sibling tools like entity_profile or compare_entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit usage guidance: 'Use FIRST whenever you have a name but need an ID', lists supported input types (company, drug), and explains edge-case behavior such as ambiguous instrument names returning figi_candidates. It also tells the agent exactly what to pass and what not to pass ('never the question's full noun phrase'). This is strong when-to-use guidance, even though it does not name alternative lookup tools.

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
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are essentially the same router with different output modes, and ask_pipeworx_beta is currently identical to ask_pipeworx. There is also meaningful overlap between entity_profile, compare_entities, recent_changes, validate_claim, and the USAspending profile/search tools.

Naming Consistency3/5

All names are lower_snake_case with useful prefixes like ask_, polymarket_, and usa_, which helps grouping. However, the underlying convention is mixed: some are verb+object, some are noun phrases, and some are bare verbs, so there is no uniform verb_noun pattern.

Tool Count2/5

38 tools is well beyond the heavy range, and most of them are unrelated to USAspending: Polymarket betting, npm dependency checks, AI visibility, memory storage, and meta-tools. The actual USAspending-specific surface is only about seven tools, making the server feel bloated and unfocused.

Completeness3/5

The federal-contract cluster covers award search, recipient/incumbent profiles, expiring awards, and spending by agency/category/trend, which handles the main contracting questions. Missing award-detail retrieval, grants/assistance coverage, and open-solicitation lookup, which usa_expiring_awards explicitly punts to external samgov/govcon tools.

Resources