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Glama

Uuid

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?

With annotations already declaring readOnly/idempotent/openWorld, the description goes well beyond them: it documents graceful degradation ('if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return'), internal cascading across endpoints, return of an explicit `unresolved` field, and a citation format for drug lookups. No contradiction with 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 but logically front-loaded: example queries, then purpose, then supported types, then failure/degradation behavior. A few clauses (e.g., 'using resolve_entity replaces 2-3 manual lookups') are motivational rather than operational, keeping it from a perfect 5.

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?

Despite having no output schema, the description tells the agent what will be returned (sources per identifier, unresolved handling), what inputs are acceptable, and what happens when upstream services fail. This is fully sufficient for an agent to invoke the 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%, but the description adds meaning to the `type` enum by specifying exactly which identifiers each type yields (CIK/ticker/company_name, LEI and parent/ultimate-parent/children, FIGI, RxCUI/ingredient/brand). For `value`, it explains risky edge cases like non-ticker bond issuers and entity-name-only input. This exceeds the baseline schema coverage.

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 phrasings ('What's the ticker for…', 'find the CIK for…') and then states a precise verb+resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' This sharply distinguishes resolve_entity from siblings like entity_profile or compare_entities — it is the name-to-ID lookup step, not enrichment or comparison.

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 an explicit trigger: 'Use FIRST whenever you have a name but need an ID,' and enumerates supported types with coverage limits. It does not name a when-not-to-use case or directly contrast with an alternative tool, so it stops short of a full 5.

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
Disambiguation3/5

Most tools have distinct purposes, but several overlap heavily: ask_pipeworx_beta explicitly matches ask_pipeworx exactly, ai_visibility_check is a single-entity version of scan_competitor_ai_presence, and multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk) share fuzzy boundaries. The lengthy descriptions help, but an agent could easily select the wrong tool.

Naming Consistency4/5

The overwhelming majority follow a clear verb_noun or noun_phrase pattern (ask_pipeworx, resolve_entity, validate_claim, generate_uuid, discover_tools). The ask_pipeworx_beta/ask_pipeworx_grounded variants and brand-name tools like pipeworx_trending are minor deviations, but the overall convention is remarkably consistent across 33 tools.

Tool Count2/5

33 tools is heavy for a single MCP server, especially one named 'Uuid' where only 2 tools (generate_uuid, validate_uuid) relate to the apparent name. The remaining 31 tools constitute a sprawling data-research platform that would normally be its own server. The count stretches beyond what is typically coherent.

Completeness3/5

As a data-research platform, the surface is quite complete: lookup, grounded answers, deep research, entity resolution, comparisons, claim validation, subscriptions, memory, and feedback are all covered. However, for the nominal 'Uuid' domain, only generation and validation exist — missing anything like UUID namespace generation, timestamp extraction from v1/v7, or bulk operations — creating a glaring mismatch between server name and actual tool scope.

Resources