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Glama

Poetrydb

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

A4.8/5.0
Behavior5/5

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

Despite already having readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, the description adds substantial behavioral context: graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` reporting instead of silent omission, ambiguous bond matches returning `figi_candidates`, and internal cascading across multiple lookup endpoints. These behaviors are not inferable from annotations alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but highly structured and dense with information: examples up front, then supported types, then caveats and degradation behavior. Every sentence adds operational value, and the semicolon-separated lists keep related facts together without redundancy.

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 two-parameter tool with no output schema, the description covers inputs, return values, ambiguity handling, failure modes, and integration with downstream use ('other tools require as input'). An agent has enough context to decide when and how to call it 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%, but the description goes far beyond the schema by clarifying accepted input forms (ticker, CIK, ISIN, name), giving concrete examples like 'CH0038863350' and 'ozempic', and warning against including full noun phrases for bond issues. 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 states a very specific verb-resource pair: resolve a user-spoken NAME to the canonical/official identifiers other tools require. It supports this with concrete user utterances and enumerates exactly which identifiers are returned (CIK, ticker, LEI, FIGI, RxCUI), making the purpose unmistakable and distinct from siblings like entity_profile or validate_claim.

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 gives an explicit directive, 'Use FIRST whenever you have a name but need an ID,' and includes numerous trigger examples. It does not explicitly name sibling tools or state when NOT to use it, but the 'Use FIRST' instruction and the detailed scenarios provide strong situational 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
Disambiguation2/5

Multiple tools have overlapping purposes, especially around Polymarket and Pipeworx (e.g., bet_research, polymarket_arbitrage, polymarket_edges, polymarket_kalshi_spread). Agents are likely to misselect among these similar tools.

Naming Consistency3/5

Naming conventions are mixed: some follow verb_noun (list_authors, search_poems), while others use more complex patterns (ai_visibility_check, scan_competitor_ai_presence). Still generally readable.

Tool Count2/5

30 tools is high but not extreme; however, the server name suggests a poetry database, yet only 4 of 30 tools relate to poetry. The scope is far too broad and mismatched, making the count inappropriate for the stated purpose.

Completeness2/5

For the poetry domain, only read/search operations are present, lacking any management tools. The other domains are covered by many tools but not cohesively integrated. The set has significant gaps relative to the server's stated name.

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