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Glama

Openreview

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 declare read-only/idempotent/non-destructive, and the description adds substantial behavioral context: cascading internal lookups, graceful degradation of LEI/FIGI enrichment, ambiguity handling via figi_candidates, explicit unresolved fields, and source-labeling of identifiers. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but dense and purpose-driven; it front-loads the core purpose and examples before diving into type-specific details. Some redundancy remains (e.g., repeated name-to-ID phrasing), but for a dual-type tool with complex edge cases, the length is largely justified.

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 carries the burden of explaining return behavior, and it does cover unresolved fields, figi_candidates, and drug return contents. Still, it lacks an explicit response shape or field-level list, leaving a minor gap for agents needing to parse results.

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?

Input schema covers both parameters at 100%, but the description goes significantly further: it maps the 'value' parameter to distinct input forms (ticker, CIK, name, brand/generic), explains ISIN-to-LEI resolution, and warns against including security-class words. This prevents common misuse beyond what the schema states.

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?

States a specific verb ('resolve'), a clear resource (user-spoken names to canonical/official identifiers), and enumerates supported types and the exact identifiers produced (CIK, LEI, FIGI, RxCUI). The framing 'identifiers other tools require as input' sets it apart from ID-consuming siblings like entity_profile.

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?

Provides an explicit usage cue: 'Use FIRST whenever you have a name but need an ID,' plus a rich set of example phrasings. It also gives detailed do/don't input rules (e.g., entity name only, never a full noun phrase). However, it does not name alternatives or state when-not-to-use, so it falls short of full discriminative 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

A3.5/5.0
Disambiguation2/5

The tool set includes both OpenReview-specific tools (e.g., get_paper, list_submissions) and a large number of unrelated tools for Pipeworx, Polymarket, and SEC filings. While individual descriptions are clear, the mix of domains creates confusion about which tools to use for a given task, leading to potential misselection.

Naming Consistency2/5

Tool names follow inconsistent patterns: some use underscore_case (ai_visibility_check, generate_llms_txt), some are verb_noun (get_paper, list_venues), and others use descriptive phrases (ask_pipeworx_grounded, polymarket_arbitrage). The lack of a unified naming scheme makes the tool set feel disjointed.

Tool Count2/5

With 37 tools, the count is too high for a server supposedly focused on OpenReview. Only about 6 tools are directly related to OpenReview; the rest are for unrelated domains like prediction markets, data retrieval, and memory management. The scope is unclear and overloaded.

Completeness2/5

For the OpenReview domain, the tool set covers basic retrieval (get_paper, search_notes, list_submissions) but lacks operations like creating or updating notes, which are common in a review platform. The inclusion of many non-OpenReview tools does not compensate for these gaps, leaving the surface incomplete for its stated purpose.

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