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Glama

Wikimedia Rest

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

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses crucial behaviors: ambiguous names return `figi_candidates` instead of asserting a single answer, unresolved identifiers are explicitly listed under `unresolved`, LEI/FIGI enrichment degrades gracefully when upstream sources fail, and every identifier is labelled with its source. These details materially shape agent expectations and are not visible from the annotations alone.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and front-loaded with query examples, but the company type is expressed as one very long run-on sentence with multiple nested parentheticals, making it hard to scan. Some redundancy exists (e.g., 'other tools require as input' and 'replaces 2-3 manual lookups' say similar things). The detail is valuable, but better bulleted or segmented structure would improve readability.

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?

Given there is no output schema, the description carries the burden of explaining return values, and it does so thoroughly: it names all returned identifiers, explains ambiguous-match behavior, explicitly covers unresolved results, describes fallback degradation, and even includes the citation format for drug lookups. An agent has enough context to call the tool correctly and interpret the result.

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 the schema already describes both parameters with 100% coverage, the description adds substantial semantic value: accepted formats for `value` (ticker, CIK, ISIN, name; brand/generic drug name), the warning to pass only the entity name, the explanation that trailing security-class words break FIGI matching, and the ISIN-to-LEI mapping behavior. This goes far beyond the schema's simple type/example descriptions.

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 natural-language queries and then states the precise purpose: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It clearly distinguishes this tool as an identifier resolver rather than a profiling or validation tool, and it enumerates supported types (company, drug) and the identifiers returned.

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?

It explicitly says 'Use FIRST whenever you have a name but need an ID,' which is strong, actionable guidance. It also notes that the tool replaces 2-3 manual lookups and describes the internal cascading behavior. However, it does not explicitly name sibling tools as alternatives or state when not to use it, leaving some routing inference to the agent.

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

A3.6/5.0
Disambiguation2/5

The set mixes several overlapping families—ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded, six Polymarket tools, ai_visibility_check/scan_competitor_ai_presence, and the memory tools—so an agent can easily misselect. Although many descriptions are rich, the tool boundaries are not distinct enough, and two tools are explicitly near-identical at present.

Naming Consistency3/5

Names are all lowercase snake_case with some logical prefixes (page_*, polymarket_*, pipeworx_*), but conventions mix imperative verbs (remember, forget, subscribe), bare nouns/adjectives (random, featured, onthisday), and descriptive noun phrases (entity_profile, page_html). The pattern is readable but not consistent enough to predict tool names reliably.

Tool Count2/5

42 tools is far beyond the well-scoped range, especially given that the server is labeled 'Wikimedia Rest' but most tools target Pipeworx data research, prediction markets, npm scanning, memory, and AI marketing. Many tools could be consolidated or split into separate servers.

Completeness3/5

For reading Wikipedia content the page_* tools are fairly complete, and the Pipeworx side covers ask/research/resolve/validate workflows. However, major gaps exist for a coherent user: no Wikipedia search/resolve-title tool, no article diff or edit workflow, and the 'Wikimedia Rest' server lacks any write or query surface matching its name; the overall domain is so diffuse that completeness is hard to assess.

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