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Glama

Amtrak

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 indicate read-only, idempotent, open-world behavior, but the description adds substantial behavioral context: identifiers are source-labelled, unresolved identifiers appear under `unresolved`, LEI/FIGI enrichment degrades gracefully, calls cascade through multiple lookup endpoints, and non-US/non-equity coverage is explained. This goes well beyond the structured 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 long and densely packed with valuable detail, and it is front-loaded with example queries and 'Use FIRST'. However, the heavy nested parentheses and run-on structure make it harder to scan than necessary; bullets or shorter sentences would improve readability without losing content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and the absence of an output schema, the description explains return components well: CIK, ticker, company_name, LEI, ownership, FIGI, RxCUI, ingredient, brand, source labels, and `unresolved` handling. A notable gap is the lack of explicit guidance on how ambiguous name matches are returned or disambiguated, which would be valuable for a resolver tool.

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?

Schema coverage is 100%, but the description adds critical semantics beyond the schema: it documents ISIN as an accepted company input, requires passing the entity name only, warns against trailing security-class words for bonds, and clarifies brand vs generic drug names. This materially improves correct invocation.

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 the exact verb+resource: resolving a user-spoken NAME into canonical/official identifiers that other tools need. Example queries like "find the CIK for..." and "what's the LEI for..." anchor the purpose concretely, and "Use FIRST whenever you have a name but need an ID" clearly positions it relative to sibling 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?

The description gives explicit guidance to use this tool first when a name needs to become an ID, and it enumerates accepted company and drug inputs. It does not explicitly name alternative tools or state when NOT to use it, so it falls just short of the strongest usage 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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all answer questions; polymarket_edges and polymarket_arbitrage both find opportunities; and memory tools (remember, recall, forget) are separate but simple. This makes it hard for an agent to pick the right tool without careful reading.

Naming Consistency2/5

Naming is inconsistent: some tools use verb_noun (generate_llms_txt, discover_tools), others noun_noun (entity_profile, amtrak_station_info), and some are single verbs (remember, forget). The Amtrak tools follow a pattern but the rest do not, leading to a chaotic mix.

Tool Count2/5

With 35 tools but only 4 actually related to Amtrak, the server is severely over-scoped for its stated purpose. The majority of tools are unrelated, making it feel bloated and misnamed.

Completeness1/5

For an Amtrak server, the tool surface is severely incomplete: lacks booking, schedules, ticket info, delay details beyond worst delay, and station amenities. The tiny Amtrak subset is a mere fraction of what users would expect.

Resources