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Glama

Data Nantes

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?

Despite readOnlyHint and idempotentHint annotations, the description adds substantial behavioral context: it cascades through multiple lookup endpoints, replaces 2–3 manual lookups, degrades gracefully when GLEIF/OpenFIGI are unavailable, returns `figi_candidates` on ambiguity, and explicitly reports unresolved identifiers. This goes far beyond what annotations alone 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 and dense, with many nested parentheticals, but it is front-loaded with purpose and usage guidance before diving into per-type details. Every major clause earns its place; the density reduces readability slightly, but there is no filler or tautology.

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 complex multi-source lookup with no output schema, the description covers input semantics, supported types, ambiguity behavior, unresolved identifier reporting, source labeling, ISIN-to-LEI mapping, and graceful degradation. An agent has enough context to select and invoke the tool correctly and to interpret common edge cases.

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 high-value guidance: the `value` parameter must be the entity name only, bond issuer names must be passed exactly as printed, and trailing security-class words should be omitted because they match nothing. This kind of error-preventing detail is exactly what the description should add beyond the schema.

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 and resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It then enumerates concrete example queries and the supported entity types ('company', 'drug'), making it unmistakable what this tool does and how it differs from tools that consume identifiers rather than produce them.

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 usage rule: 'Use FIRST whenever you have a name but need an ID.' This clearly identifies the triggering condition. It does not name specific sibling tools to avoid, but the condition 'other tools require as input' implies the tool is a lookup prerequisite rather than a profiling or comparison tool.

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

A4.1/5.0
Disambiguation3/5

Many tools have overlapping purposes, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and Polymarket analysis tools (bet_research, polymarket_edges, polymarket_arbitrage). While descriptions are detailed, the similarity in function could confuse an agent trying to select the right one.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern (e.g., ai_visibility_check, ask_pipeworx, bet_research, compare_entities). No mixing of conventions (camelCase, PascalCase) is observed, making it predictable.

Tool Count3/5

With 34 tools, the server is on the heavy side. The majority are Pipeworx tools covering many domains, but the count exceeds the typical sweet spot of 3-15 tools. Some consolidation could reduce redundancy.

Completeness4/5

The tool surface covers a wide range of data access and analysis (Nantes open data, SEC filings, Polymarket, entity comparison, dependency scanning). Minor gaps exist, such as limited Nantes data operations (only query and info) and some tools requiring external accounts, but the overall scope is comprehensive.

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