Skip to main content
Glama

Data Centrevaldeloire

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?

The description goes well beyond the annotations, detailing graceful degradation ('if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return'), ambiguous-match behavior ('asserts nothing and returns `figi_candidates`'), and explicit unresolved handling ('stated explicitly under `unresolved`'). It also discloses internal cascading lookups and source labeling, providing rich behavioral context.

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 dense and well-structured: it front-loads the core purpose and usage rule, then uses clear labels ('SUPPORTED TYPES', 'LEI/FIGI enrichment') to organize complex details. Every sentence delivers actionable information, and the length is justified by the tool's scope and edge cases.

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?

With no output schema present, the description carries the burden of explaining return behavior. It does so thoroughly: mentions `figi_candidates`, `unresolved`, source-labeled identifiers, fallback behavior, and the drug-type return fields (RxCUI, ingredient, brand). This is complete enough for an agent to call the tool correctly and interpret results.

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 description coverage is 100%, the description adds critical parameter semantics beyond the schema, especially for `value`: 'Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed... never the question's full noun phrase.' It also elaborates on the `type` parameter by explaining what each type resolves to and which input formats are accepted (ticker, CIK, ISIN, name).

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 a specific, unambiguous statement of purpose: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It also enumerates supported types (company, drug) and gives example queries, making it immediately clear what this tool does and that it is the name-to-ID resolver among the sibling tools.

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 provides a strong when-to-use directive: 'Use FIRST whenever you have a name but need an ID.' It also gives concrete trigger examples and warns about the correct way to pass bond issuer names. However, it does not explicitly name alternatives or state when-not-to-use scenarios, so the guidance is clear but not fully exhaustive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Data Centrevaldeloire toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

Several tools have overlapping boundaries, especially ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim, all of which route through the same underlying source catalog. The beta variant is explicitly identical to the stable router right now, which forces agents to pick between tools that currently do the same thing. Many of the polymarket tools are also close enough that an agent must read long descriptions carefully to avoid mis-selection.

Naming Consistency3/5

The set is readable and mostly snake_case, but the naming conventions are mixed: some tools use verb_noun (search_datasets, compare_entities, validate_claim), some are noun-like (entity_profile, dataset_info, bet_research), and some use product prefixes (pipeworx_trending, polymarket_edges). There is no single consistent pattern, though related clusters are internally recognizable.

Tool Count2/5

Thirty-four tools is above the heavy threshold, and the apparent server purpose from the name is a regional open-data portal, which only needs the three dataset tools. The remaining tools are a sprawling Pipeworx research assistant covering prediction markets, npm dependencies, AI visibility, memory, subscriptions, and feedback, making the set feel overstuffed and poorly scoped for the stated server.

Completeness4/5

Within the broad data/research scope the coverage is quite deep: discovery, querying, grounded verification, entity profiling, comparisons, monitoring, subscriptions, memory, and prediction-market analysis all have dedicated tools. The Centre-Val de Loire core is adequately covered by search_datasets, dataset_info, and query, though a raw download or full-catalog listing endpoint is missing. The real weakness is not missing lifecycle steps but unclear boundaries between overlapping meta-tools.

Resources