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Glama

Metals Api

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 provide safety hints, and the description adds extensive behavioral detail: graceful degradation when GLEIF/OpenFIGI are unavailable, ambiguous names returning `figi_candidates`, unresolved identifiers explicitly listed under `unresolved`, ISIN-to-LEI mapping for non-US issuers, and output labels indicating source provenance. This goes far beyond what the annotations convey.

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 but tightly organized with clear sections (examples, supported types, caveats, degradation behavior). It front-loads the core purpose and the 'Use FIRST' directive. Some sentences are dense and could be trimmed, but the complexity of the tool justifies the length.

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 2-parameter tool with no output schema, the description covers the essential invocation context: supported input types, what identifiers each type returns, edge cases like ambiguous matches and unresolved identifiers, and behavior under downstream service failures. An agent has enough information to select and call this tool correctly.

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% and the schema already describes both parameters, but the description adds substantial extra meaning through examples, accepted input forms for the `value` parameter, the entity-type breakdown, and the critical warning against passing full noun phrases for bonds. This is high-value enrichment, not redundancy.

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 action and resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It distinguishes itself from sibling tools by positioning itself as the prerequisite lookup step and by listing supported entity types. The concrete examples ('ticker', 'CIK', 'LEI', 'RxCUI') make the scope unmistakable.

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 explicitly says 'Use FIRST whenever you have a name but need an ID,' giving a clear trigger condition. It clarifies that internal cascading replaces 2-3 manual lookups, implying it is the appropriate single entry point. It does not explicitly name alternatives or exclusion cases, but the guidance is sufficiently clear for an agent to select it correctly.

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

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are variants of the same router, and the six polymarket_* tools cover overlapping arbitrage/edge analysis territory. ai_visibility_check vs scan_competitor_ai_presence and discover_tools vs suggest_questions add further boundary ambiguity. While descriptions try to differentiate, an agent could easily misselect among these clusters.

Naming Consistency3/5

Most names are readable snake_case, but there is no consistent verb_noun pattern: verbs vary (ask, get, list, scan, search, suggest, validate, generate, compare) and several tools are named by product prefix (pipeworx_*, polymarket_*) rather than by action. The pattern is predictable within clusters but inconsistent across the set.

Tool Count2/5

33 tools is heavy for the server's stated name, 'Metals Api', which only has two metals-related tools (get_historical, get_latest). Even as a general data-research server, the surface is bloated with memory utilities, subscription management, feedback, trending, and unrelated AI-visibility scanning. The scope mismatch makes the count feel unjustified.

Completeness3/5

The core metals domain is thin: latest and single-date historical prices exist, but there is no time-series range query, no list of supported metals, and no explicit currency conversion endpoint. The broader data-research/subscription/memory surface is relatively complete, but it is disconnected from the server's apparent purpose, leaving notable gaps for a metals-focused agent.

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