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Samgov

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 declare readOnlyHint, idempotentHint, and non-destructive intent; the description adds substantial behavioral context beyond that: unresolved identifiers are 'stated explicitly under `unresolved` rather than omitted', enrichment 'degrades gracefully' if GLEIF/OpenFIGI are unavailable, internal calls cascade through multiple endpoints, and ambiguous matches return `figi_candidates`. This is exactly the kind of runtime nuance an agent needs.

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 long, but nearly every clause carries essential information: supported types, ambiguous-match behavior, unresolved handling, and graceful degradation. The opening user-phrase examples are excellent front-loading. The main weakness is the run-on parenthetical structure, which could be broken into clearer bullets or sentences without losing fidelity.

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 tool with no output schema, the description thoroughly covers what the agent will receive: sourced identifiers, `unresolved` field, `figi_candidates` for ambiguity, drug result shape including RxCUI and citation, and failure-mode behavior when upstream services are down. Combined with full schema coverage and safety annotations, nothing critical is missing for correct invocation.

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?

Even though schema description coverage is 100%, the description adds critical parameter semantics not present in the schema: the `value` parameter's warning to 'Pass the ENTITY NAME ONLY' and to avoid 'the question's full noun phrase', with bond-issuer examples. It also explains what each `type` accepts and how ISIN input behaves, materially improving the agent's ability to input correct values.

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 phrasings ('What's the ticker for…' / 'find the CIK for…') and immediately states the core action: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It explicitly enumerates supported entity types and the identifiers returned per type, which clearly distinguishes this resolver from sibling tools like entity_profile or sam_entity_search.

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 gives a strong trigger directive: 'Use FIRST whenever you have a name but need an ID.' It also clarifies when ambiguity arises ('when a name matches more than one instrument it asserts nothing and returns figi_candidates'). However, it does not name specific sibling alternatives or state when NOT to use this tool, so the guidance stops short of explicit exclusion.

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

Three ask_pipeworx variants and a dense cluster of polymarket_* edge tools have heavily overlapping purposes, and ai_visibility_check vs scan_competitor_ai_presence further blurs boundaries. Only the sam_*, memory, and subscription tools form cleanly distinct families.

Naming Consistency3/5

All names are lowercase snake_case and readable, but the pattern is mixed: verb_noun names (compare_entities, resolve_entity), bare verbs (remember, recall, forget), noun phrases (entity_profile, polymarket_edges), and domain-prefix families (sam_*, polymarket_*) coexist. No camelCase chaos, but no consistent verb style either.

Tool Count2/5

36 tools is well beyond the ideal range, and many are near-duplicates or wrappers (ask_pipeworx variants, ai_visibility_check vs scan_competitor_ai_presence). The server is named Samgov, yet only 5 tools actually concern SAM.gov, making the count feel inflated and unfocused.

Completeness3/5

The SAM.gov subset covers entity search, opportunities, set-asides, opportunity details, and exclusions, but omits major datasets like contract awards. The broader Pipeworx research/memory/subscription surface is extensive, though it is muddled by redundant query modes and lacks a direct way to invoke individual pack tools.

Resources