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Glama

Congressional Documents

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

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool read-only, idempotent, and non-destructive, and the description adds substantial operational detail: internal cascading lookups, graceful degradation of LEI/FIGI enrichment, and explicit `unresolved` reporting rather than omission. No statement contradicts the annotations.

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 front-loaded with example queries and the 'Use FIRST' guidance, and nearly every clause adds value. However, the company portion is an extremely long, dense run-on with deep parentheticals, making it harder to parse than necessary despite being information-rich.

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?

Even without an output schema, the description names the returned identifiers for each type (CIK, ticker, company_name, LEI, ownership, FIGI; RxCUI, ingredient, brand, citation), explains accepted input formats, and covers failure behavior such as graceful degradation. It is sufficiently complete 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?

The schema covers both parameters, but the description goes far beyond it: company accepts ticker, CIK, ISIN, or name; drug accepts brand or generic names; and the issuer-only warning explains why trailing security-class words should be excluded. This meaningfully improves parameter understanding.

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 queries and then states the exact operation: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It clearly distinguishes this tool from siblings like entity_profile or compare_entities by focusing on name-to-identifier resolution across company and drug types.

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 provides an explicit trigger: 'Use FIRST whenever you have a name but need an ID' and notes that the tool replaces 2-3 manual lookups, which reinforces when to select it. However, it does not explicitly name sibling alternatives or state when not to use it, so it stops short of 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
Disambiguation1/5

Several tools are near-duplicates: ask_pipeworx and ask_pipeworx_beta are identical in practice, ai_visibility_check and scan_competitor_ai_presence overlap, and the cluster of polymarket_* tools plus bet_research creates unclear boundaries. Only the three congressional-document tools have clearly distinct purposes.

Naming Consistency2/5

All names use snake_case, but the verbs are inconsistent: some are verb-first (search_, get_, list_, generate_, scan_), many are noun-first (entity_profile, polymarket_arbitrage, ai_visibility_check), and a few are bare single words (remember, recall, forget). The ask_pipeworx* family is internally consistent, but the overall set has no predictable pattern.

Tool Count2/5

34 tools is far too many for a server named 'Congressional Documents' — only 3 of them actually relate to congressional documents. The rest form a broad general-purpose data and analytics toolkit that seems pasted in without relation to the server's stated scope.

Completeness4/5

For the congressional-documents subset, the surface is complete: search, retrieve full text, and enumerate document types cover the read-only use case. For the broader data-platform domain the tools actually address, coverage is also strong (grounded answers, deep research, entity resolution, comparisons, subscriptions). The main issue is the mismatch between the server title and the actual tool mix, not missing functionality.

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