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Glama

Wmata

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

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses many non-obvious behaviors: graceful degradation when GLEIF/OpenFIGI is unavailable, explicit `unresolved` reporting rather than omission, source-labelling of each identifier, and candidate-return behavior on ambiguity. It also notes that the tool cascades through multiple endpoints internally, which sets accurate expectations for latency and scope.

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 dense, but nearly every sentence carries unique information about behavior, supported identifiers, or failure modes. It front-loads the core directive and examples before diving into details, though the single large paragraph and heavy parentheticals make it slightly harder to scan than an ideally structured description.

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?

Given the tool's complexity and the absence of an output schema, the description is remarkably complete: it explains what identifiers come back for each type, what happens on unresolved or ambiguous matches, and what degrades if upstream sources fail. An agent has enough information to call the tool correctly and interpret its likely outcomes.

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 schema coverage is 100%, the description adds substantial semantic value: it specifies accepted input forms for `value` (ticker, CIK, ISIN, or company name), gives concrete examples, and explains the issuer-name-only rule for bond lookups. For `type`, it details exactly what 'company' and 'drug' resolve to, which the enum alone does not convey.

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 states a specific verb ('resolve') and resource ('user-spoken NAME to the canonical/official identifiers other tools require as input'), and enumerates exact supported entity types and identifier systems (CIK, LEI, FIGI, RxCUI). It distinguishes itself from siblings by framing the tool as the name-to-ID entry point that other tools depend on, and by noting it covers non-equity instruments that ticker-based lookups miss.

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 explicitly says 'Use FIRST whenever you have a name but need an ID,' giving an unambiguous triggering condition. It also provides negative guidance: when a name matches multiple instruments it returns candidates rather than asserting a single answer, and it warns against passing full noun phrases for bond lookups. This is clear when-to-use and when-not-to-use 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

A3.7/5.0
Disambiguation3/5

Most tools have distinct purposes with detailed descriptions, but the server mixes WMATA transit tools (bus routes, rail predictions) with unrelated general-purpose tools (deep_research, polymarket_arbitrage). This broad scope can confuse an agent expecting a focused transit server.

Naming Consistency2/5

All names use snake_case, but there is no consistent verb_noun pattern. Many are noun_noun (bus_routes, rail_lines) or verb_properNoun (ask_pipeworx), and the naming conventions vary wildly across different domains (e.g., validate_claim vs entity_profile vs scan_dependency).

Tool Count2/5

41 tools is excessive for a WMATA transit server. The vast majority of tools (ask_pipeworx, deep_research, polymarket_*) are not related to WMATA, making the tool surface bloated and unfocused for its stated purpose.

Completeness3/5

The WMATA-specific tools cover core bus and rail operations (incidents, predictions, routes, stations). However, gaps like fare info and elevator status are missing. The non-transit tools are extensive but irrelevant to the server's apparent focus.

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