Skip to main content
Glama

Opendatasoft

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

A4.9/5.0
Behavior5/5

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

Annotations already cover readOnly, openWorld, idempotent, and non-destructive hints. The description goes far beyond them by disclosing: graceful degradation when GLEIF/OpenFIGI unavailable, behavior when a name matches multiple instruments (returns figi_candidates, asserts nothing), explicit `unresolved` field for failed lookups, and the ISIN-to-LEI mapping for non-US issuers. This is exceptional behavioral context that directly informs agent decision-making.

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 every sentence earns its place given the complexity of the tool. It front-loads the primary purpose and usage rule, then systematically covers supported types, behavioral details, and graceful degradation. It could be trimmed slightly without losing meaning, but the density is high and structure is logical.

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 tool with no output schema, the description fully compensates: it describes return behavior (figi_candidates, unresolved), failure modes, and enrichment fallbacks. It answers likely agent questions such as 'what happens if the identifier cannot be resolved?' and 'what if multiple instruments match?'. Combined with the annotations, nothing critical is missing for correct invocation and response interpretation.

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 crucial parameter-level nuance beyond the schema. For the `value` parameter, it explicitly instructs passing the ENTITY NAME ONLY and warns against including trailing security-class words with a concrete example ('NEW YORK ST DORM AUTH' vs 'revenue bonds'). This prevents a common error and shows deep semantic understanding.

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 uses a clear verb-resource pairing: 'resolve a user-spoken NAME to the canonical/official identifiers'. It explicitly distinguishes between supported entity types ('company' vs 'drug') and enumerates the exact identifiers produced (CIK, LEI, FIGI, RxCUI). This makes it unmistakable what the tool does and how it differs from siblings like entity_profile or compare_entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states 'Use FIRST whenever you have a name but need an ID', providing a clear trigger condition. It also explains internal cascading behavior ('replaces 2-3 manual lookups') and details edge cases like non-equity instruments resolving via name search. This gives an agent explicit guidance on when to invoke this tool over alternatives.

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

All Opendatasoft 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

A3.6/5.0
Disambiguation2/5

The tool set is a kitchen sink of unrelated utilities (Opendatasoft catalog, Pipeworx data search, prediction markets, npm scanning, memory, etc.). The 'ask_pipeworx' family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are very similar and could easily be confused. The wide variety of purposes with overlapping names makes it hard for an agent to disambiguate.

Naming Consistency1/5

Naming is wildly inconsistent: snake_case (ai_visibility_check, ask_pipeworx), concatenated (pipeworx_trending, polymarket_arbitrage), verb phrases (compare_entities, suggest_questions), and simple nouns (dataset, records). No consistent pattern exists, making it hard to predict tool names.

Tool Count2/5

At 36 tools, the server is overloaded with a scattershot collection of capabilities unrelated to its name (Opendatasoft). Only 5 tools directly relate to Opendatasoft, while the rest cover diverse third-party services. This indicates poor scope focus.

Completeness2/5

The server lacks completeness for any single purpose. For Opendatasoft, it has only read-oriented tools with no create/update/delete. For Pipeworx, many query tools exist but no data ingestion. Prediction market tools are extensive but not part of the core mission. Overall, the surface has significant gaps.

Resources