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Glama

Bright Data

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

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description reveals substantial behavioral details: internal cascading through multiple endpoints, graceful degradation when GLEIF/OpenFIGI are unavailable, returning figi_candidates instead of asserting on ambiguous matches, explicitly listing unresolved identifiers under `unresolved`, and labeling each identifier with its source. No contradiction with 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 is dense and long, with a run-on opening that packs in many example queries before stating the purpose. It is organized with 'SUPPORTED TYPES' and front-loaded usage, but several behaviors could be summarized more tersely. Every sentence adds information, yet the length strains the 1-5 'appropriately sized' criterion.

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?

Despite lacking an output schema, the description explains what is returned in key scenarios: canonical identifiers with source labels, `figi_candidates` for ambiguous instruments, and an explicit `unresolved` list for failures. It also covers edge cases (non-US issuers, non-equity instruments, ISIN input, degradation) and both supported types. An agent has enough context to call and interpret results correctly.

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?

Schema coverage is already 100% and both parameter descriptions are detailed. The description adds the ISIN input possibility for company type (not present in the schema), clarifies that entities should be passed as names only rather than full noun phrases, and explains why trailing security-class words cause failure. This goes beyond the schema's baseline, so a 4 is warranted.

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 user utterances ("What's the ticker for…", "find the CIK for…") and states the core function: resolve a user-spoken NAME to canonical/official identifiers. It also distinguishes itself from downstream tools by noting these identifiers are what 'other tools require as input,' making the tool's specific role unmistakable.

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 when-to-use direction: 'Use FIRST whenever you have a name but need an ID.' It also says a single call replaces 2-3 manual lookups, signaling it is the preferred entry point for name-to-ID tasks. It does not explicitly name sibling alternatives or state when-not-to-use, but the priority framing 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

A3.8/5.0
Disambiguation2/5

The ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) has significant boundary overlap, and ask_pipeworx_beta is explicitly identical to ask_pipeworx today. The six polymarket_* tools plus bet_research also cover heavily overlapping prediction-market territory, so an agent could easily route a query to the wrong one despite detailed descriptions.

Naming Consistency4/5

Most tools follow a consistent snake_case verb_noun pattern (ask_pipeworx, compare_entities, generate_llms_txt, list_subscriptions, validate_claim, scan_dependency). Minor deviations exist — entity_profile is noun-noun, brightdata_serp/brightdata_unlock use a vendor prefix, and remember/recall/forget are bare verbs — but the overall style is predictable and readable.

Tool Count2/5

At 33 tools this exceeds the 25+ threshold that signals an over-heavy surface. While the server covers multiple domains (data lookup, prediction markets, memory, subscriptions, AI visibility), many of those domains carry redundant variants that could be consolidated, making the count feel bloated rather than well-scoped.

Completeness4/5

The surface covers the core workflows well: querying (ask_pipeworx variants), deep research, entity resolution, entity profiles, comparisons, change feeds, claim verification, discovery/onboarding, subscriptions (list/subscribe/unsubscribe), memory (remember/recall/forget), and feedback. Minor gaps exist — there is no direct tool to fetch a returned pipeworx:// citation URI, and memory lacks an explicit update operation — but agents can work around these.

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