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Pexels

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

A4.7/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior, and the description adds valuable behavioral nuance: graceful degradation of LEI/FIGI enrichment, explicit `unresolved` handling, `figi_candidates` when a name maps to multiple instruments, and internal cascading through multiple lookup endpoints. This is exactly the kind of context that helps an agent trust and interpret results.

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 and front-loaded with examples and the primary usage rule before diving into supported types. Every clause adds operational detail, though the format is somewhat sprawling and could be tightened without losing content.

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 the lack of an output schema, the description fully prepares the agent for what happens across both entity types, including ambiguous matches, unresolved identifiers, and degraded enrichment. It covers acceptance criteria, edge cases, and expected outputs, leaving no critical gap for correct selection and invocation.

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?

While the schema already covers both parameters, the description adds substantial meaning: accepted input forms for company (ticker, CIK, ISIN, name), drug brand/generic examples, and a crucial warning to pass only the entity name, not the full noun phrase, with a concrete bond example. This materially improves correct invocation.

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 precise verb+resource: resolving a user-spoken name to canonical/official identifiers. It immediately gives concrete query paraphrases and explicitly tells the agent to use it whenever a name is present but an ID is required, clearly separating 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 provides clear context: use this first whenever a name needs to be converted to an identifier, and it explains that it replaces 2-3 manual lookups. It does not explicitly name alternatives or exclusion conditions, but the trigger examples and 'Use FIRST' guidance are strong enough for correct selection.

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
Disambiguation3/5

Many tools are clearly from distinct domains (Pexels media, Pipeworx data, Polymarket betting, utility), but within each domain there is significant overlap (e.g., ask_pipeworx vs ask_pipeworx_grounded vs deep_research, or numerous polymarket tools). Detailed descriptions help differentiate, but some tools could still be confused.

Naming Consistency3/5

Naming conventions vary: some tools use verb_noun (ask_pipeworx, compare_entities), others are single-word (photo, video), and some mix patterns (photo_curated vs photo_search). The Pexels subset is consistent, but the overall set includes tools from other ecosystems with different styles.

Tool Count2/5

With 38 tools, the set is bloated for a Pexels server. Only 8 tools are actually Pexels-related; the rest are Pipeworx data query, Polymarket betting, and utility tools. This mismatch makes the count inappropriate for the stated server purpose.

Completeness4/5

For the Pexels domain, the tool set covers search, fetch, and collections for both photos and videos, which is adequate. The unrelated tools add bloat but don't create gaps in the Pexels functionality. Minor missing features like uploads or editorial content are not critical.

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