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wikipedia

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. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

TDQS

A4.7/5.0
Behavior5/5

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

The description richly discloses behavior beyond the annotations: it explains cascading internal lookups, graceful degradation when LEI/FIGI enrichment is unavailable, ambiguity handling via figi_candidates, explicit unresolved identifiers, and source-labelled results. This goes well beyond the readOnly/openWorld/idempotent 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 front-loaded with purpose and user-phrase examples, then organized by entity type. It is longer than strictly necessary and uses dense parentheticals, but most details are relevant to correct invocation, so it earns a high but not maximal score.

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 is unusually complete: it covers input normalization, supported types, failure modes, edge cases, enrichment degradation, and what the caller should expect in ambiguous or unresolved situations. Nothing essential for correct invocation is missing.

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?

Even though schema coverage is 100%, the description adds significant meaning for the value parameter: examples (AAPL, CIK, ISIN), the instruction to pass only the entity name, and a concrete bond-issuer example warning that trailing security-class words will fail. This clarifies semantics far 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 purpose: resolving a user-spoken name to canonical/official identifiers that other tools require. It also differentiates itself by noting 'Use FIRST whenever you have a name but need an ID,' which distinguishes its role from sibling tools like entity_profile and 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 Guidelines4/5

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

The description gives clear usage context: 'Use FIRST whenever you have a name but need an ID' and enumerates supported entity types. However, it does not explicitly state when not to use the tool or name alternative siblings, so the guidance is strong but lacks exclusionary clarity.

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

A3.7/5.0
Disambiguation2/5

The server is named 'wikipedia' but most tools are unrelated Pipeworx/Polymarket tools, so an agent asked to use Wikipedia tools will face a large misleading option set. Even within families there is blurriness: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research overlap, and the five polymarket_* tools have closely related purposes that require reading very long descriptions to disambiguate.

Naming Consistency2/5

Naming conventions are mixed: some tools use clean verb_noun patterns (search_wikipedia, resolve_entity, validate_claim) while others use product prefixes (ask_pipeworx, pipeworx_trending, polymarket_edges) or noun-phrase names (entity_profile, recent_changes, bet_research). There is no single consistent pattern across the set.

Tool Count2/5

36 tools is already heavy, but it is especially inappropriate for a server named 'wikipedia' — only a handful are actually Wikipedia tools, while the rest belong to unrelated domains (Pipeworx data, Polymarket betting, memory, subscriptions, npm scanning). The count reflects a kitchen-sink scope rather than a focused purpose.

Completeness3/5

The Wikipedia-reading subset (search, summary, sections, extract, random) is decent but lacks editing, category, or link features. The broader Pipeworx/Polymarket surface is quite comprehensive, so completeness depends entirely on which implicit domain you judge it against; as a 'wikipedia' server it is incomplete, and as a unified data platform the scope is still incoherent.

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