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Glama

Pixabay

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?

Annotations already indicate a safe, read-only, idempotent operation, and the description adds substantial behavioral context beyond that: graceful degradation when LEI/FIGI sources are unavailable, the cascading internal lookups, explicit unresolved identifiers, ambiguous match handling via figi_candidates, and source labeling for every identifier.

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 dense and front-loaded with the core purpose and usage rule, and every section adds operational value. It is longer than many tool descriptions, but given the breadth of entity types, fallback behavior, and identifier semantics, the length is justified rather than redundant.

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 having no output schema, the description sufficiently covers what the agent needs to call the tool correctly: input formats, entity types, resolution sources, ambiguity behavior, unresolved identifier handling, failure tolerance, and the multi-lookup nature. It even educates the agent on non-equity instruments and issuer-name edge cases.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful semantic guidance beyond the schema: example values, the warning to pass only the entity name and never the full noun phrase, and the rationale that FIGI lookup matches instrument names — all of which help prevent common invocation mistakes.

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: resolving a user-spoken NAME to canonical/official identifiers. It explicitly distinguishes the tool's role ('other tools require as input', 'Use FIRST whenever you have a name but need an ID') and enumerates supported entity types, making its purpose clear even among many siblings.

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 strong when-to-use guidance with concrete user-phrase examples and the directive 'Use FIRST whenever you have a name but need an ID.' It does not explicitly name alternatives or when-not-to-use conditions, so it stops short of full exclusionary guidance.

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

B3.3/5.0
Disambiguation2/5

The tool set contains multiple near-duplicate lookups: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all do the same routing with minor variations, while deep_research and validate_claim also overlap in information retrieval. The two Pixabay search tools are distinct, but the abundance of overlapping data-lookup tools creates real ambiguity.

Naming Consistency2/5

All names use snake_case, but the pattern is inconsistent: some are verb_noun (search_images, validate_claim), others are noun-based (entity_profile, pipeworx_feedback), and variants like ask_pipeworx_beta/grounded introduce ad-hoc suffixing. The naming does not follow a single predictable convention.

Tool Count1/5

With 33 tools, the count is far excessive for a Pixabay server. Only 2 tools (search_images, search_videos) actually relate to Pixabay; the remaining 31 are unrelated Pipeworx data, Polymarket, memory, and subscription tools. The scope is a severe mismatch.

Completeness1/5

For a server named Pixabay, the surface is severely incomplete: only basic image/video search is provided, with no tool for fetching details, downloading, managing collections, or any other lifecycle operation. Meanwhile, the Pipeworx domain is over-covered, but that is irrelevant to the server's stated purpose.

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