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Glama

Newsapi

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 signal read-only, idempotent, and non-destructive behavior; the description adds rich behavioral context: graceful degradation of LEI/FIGI enrichment, explicit `unresolved` output, `figi_candidates` when a name matches multiple instruments, source-labelled identifiers, and ISIN-to-LEI mapping. No contradiction with annotations exists.

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 well-structured with clear SUPPORTED TYPES sections and front-loads the core purpose before diving into edge cases. It is long, but the density of useful caveats (bonds, non-US issuers, graceful degradation) justifies the length; almost every sentence carries operational meaning.

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 explains the key return behaviors: missing identifiers appear under `unresolved`, ambiguous matches return `figi_candidates`, and identifiers are labelled by source. It covers both supported types, input/output mapping, failure modes, and cross-source behavior, making the description sufficiently complete for a high-complexity tool.

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% and each parameter already has a description, but the tool adds substantial value by explaining the critical 'ENTITY NAME ONLY' rule, the meaning of supported enums, input formats (ticker, CIK, ISIN, name), and the consequence of including trailing security-class words. This materially improves invocation correctness beyond 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 precise verb+resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It enumerates concrete user phrasings and explicitly contrasts with sibling tools by positioning itself as the 'FIRST' stop when an ID is needed.

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 tool gives explicit when-to-use guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains what sorts of entities it covers and notes it replaces manual lookups, but it does not explicitly contrast with related siblings like entity_profile or compare_entities, so no hard exclusions are stated.

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

A3.5/5.0
Disambiguation2/5

There are several clusters of tools with overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to data sources; entity_profile, compare_entities, and recent_changes all provide company research. The descriptions are detailed, but an agent could easily misselect among these near-duplicates, especially since ask_pipeworx_beta is explicitly identical to ask_pipeworx right now.

Naming Consistency2/5

Placeholder for naming consistency placeholder

Tool Count1/5

34 tools for a server named Newsapi is an extreme scope mismatch. Only three tools (everything, top_headlines, sources) are news-related; the rest cover data research, prediction markets, memory, subscriptions, and package scanning, which belongs in separate servers. The count is also past the 25+ range that feels overloaded for any single purpose.

Completeness3/5

The three NewsAPI tools themselves cover the standard news surface (top_headlines, everything, sources) with no major gaps. However, the server's stated purpose is diluted by ~30 unrelated tools, and the non-news tools form an incoherent assortment with no clear unified domain to assess completeness against. The mismatch makes completeness hard to reason about and degrades the overall usefulness.

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