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Glama

F1

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?

Annotations already indicate readOnly, idempotent, open-world, and non-destructive behavior. The description adds substantial behavioral context: internal cascading through multiple endpoints, graceful degradation for LEI/FIGI when upstream sources fail, explicit listing of unresolved identifiers, candidate disambiguation when multiple matches exist, and source labeling for every identifier. This goes well beyond what annotations 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 but dense; nearly every clause adds operational value, and important guidance is front-loaded ('Use FIRST whenever...'). The parentheticals and repeated emphasis on non-equity handling make it slightly heavier than strictly necessary, but it avoids fluff and promotional filler.

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, the description carries the burden of explaining what the tool returns, and it does: for companies it names CIK, ticker, company name, LEI with ownership, FIGI, source labels, and unresolved fields; for drugs it names RxCUI, ingredient, brand, and a citation. Candidate behavior and graceful degradation are also covered, making the tool fully callable and interpretable.

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 significant meaning beyond the schema. It gives concrete examples for 'value' (ticker, CIK, ISIN, brand/generic name), clarifies that only the entity name should be passed with a specific bond-issuer warning, and explains how the 'type' enum maps to different lookup behaviors. This is much more than a restatement of 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 canonical/official identifiers. It enumerates supported types ('company', 'drug') and explicitly frames its role as the first-stop lookup ('Use FIRST whenever you have a name but need an ID'), clearly distinguishing it 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 explicit usage context: 'Use FIRST whenever you have a name but need an ID.' It also clarifies edge cases, such as when a bond issuer name does not identify a single instrument, and explains that non-equity instruments DO resolve here. It does not name specific alternative tools or give when-not-to-use exclusions, but the guidance is strong.

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
Disambiguation3/5

Most tools have detailed 'use when' guidance and the polymarket/entity clusters are distinguishable, but ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx/ask_pipeworx_grounded/deep_research sit close together. Many other tools (ai_visibility_check vs scan_competitor_ai_presence, bet_research vs polymarket_edges) require careful reading to keep separate.

Naming Consistency3/5

Consistent snake_case and recognizable subfamilies (ask_pipeworx*, polymarket_*, get_*) keep names readable. However the overall set mixes verb_noun (get_schedule, resolve_entity), bare verbs (remember, forget), noun phrases (recent_alerts, pipeworx_trending), and compound noun names (bet_research, polymarket_fill_risk), so there is no single predictable convention.

Tool Count2/5

35 tools is well over the 25+ threshold and spans many unrelated concerns: F1 data, universal data lookup, prediction markets, subscriptions, memory, npm scanning, and AI visibility. While a broad data platform can justify a large surface, the mix of one-off and meta tools makes this feel bloated rather than well-scoped.

Completeness2/5

For a server named F1 the surface is thin: it covers schedule, driver profiles, race results, and driver standings but lacks constructor standings, qualifying, team/circuit data, and a driver list. The surrounding Pipeworx tools provide depth in other domains, but they do not fill the F1-specific gaps.

Resources