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Glama

Fac

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

A4.4/5.0
Behavior5/5

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

Despite annotations already marking the tool read-only/idempotent/non-destructive, the description adds substantial behavior: it cascades through multiple lookup endpoints, degrades gracefully if GLEIF/OpenFIGI are down, returns ownership chains when LEI resolves, and resolves non-equity instruments that lack tickers. This goes far beyond the annotations.

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 front-loads with user-phrase examples and is information-dense, but it is a long run-on paragraph with a duplicated sentence ('Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups' appears twice). Structure could be improved with bullets per entity type.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description covers return contents for both types (CIK, ticker, company_name, LEI, parent/ultimate-parent/children, FIGI, RxCUI, ingredient, brand, citation) and explains fallback behavior. It does not explicitly state the response format for unresolvable names or rate limits, but these are minor given the completeness elsewhere.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already provides 100% parameter coverage with examples in the value description. The main description reinforces type-specific behavior (e.g., exact ticker map vs name search for company, brand/generic name for drug), adding meaning beyond the schema's structural definitions.

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 phrase — 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input' — and backs it with a list of example questions. It distinguishes itself from siblings by naming compare_entities for pairwise name matching, making the tool's scope unambiguous.

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?

Explicitly says 'Use FIRST whenever you have a name but need an ID' and gives example query patterns for when it applies. It also tells agents when not to use it ('use compare_entities for pairwise name matching'), though it does not enumerate all sibling alternatives.

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

A3.9/5.0
Disambiguation2/5

Several tools occupy the same "answer a factual question" niche: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and bet_research all route to the same underlying catalog, so an agent must parse subtle differences to pick correctly. scan_competitor_ai_presence also wraps ai_visibility_check, adding another near-duplicate. The detailed descriptions help, but the boundaries are genuinely fuzzy.

Naming Consistency3/5

The set mixes several conventions: fac_*, polymarket_*, and pipeworx_* prefixes coexist with bare verbs (remember, recall, forget, subscribe, unsubscribe) and noun phrases (entity_profile, recent_changes, bet_research). ask_pipeworx_beta/grounded use a suffix pattern while pipeworx_feedback/trending use a prefix, so there is no single predictable scheme. Still, most names are readable and describe what they do.

Tool Count2/5

36 tools is well above the 25+ threshold and creates a heavy surface for any client to load and reason about. The broad data-platform scope explains some of the count, but many tools are meta-variants of the same query/research capability rather than genuinely distinct operations.

Completeness4/5

For the server's apparent purpose—authoritative data lookup, research, prediction-market analysis, and account/feed management—the surface covers the core lifecycle: query, entity resolution, profiles, comparisons, recent changes, claim verification, subscriptions, alerts, and memory. Minor gaps exist (no direct tool to fetch a pipeworx:// citation URI; no raw per-pack access), but most workflows are supported.

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