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Glama

Unpaywall

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 annotations (readOnly, idempotent, non-destructive), the description discloses graceful degradation when GLEIF/OpenFIGI are unavailable, ambiguous-match behavior (asserts nothing, returns `figi_candidates`), and explicit listing of unresolved identifiers under `unresolved`. It also reveals internal cascading through multiple endpoints, which explains why the call may take time.

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 information-rich, but it is a long run-on with deep parenthetical nesting (e.g., the company type paragraph). It is front-loaded with examples and the core directive, yet the structure could be improved with clearer separation between the two types. Every sentence adds value, but readability suffers.

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?

Given there is no output schema, the description does a strong job of naming return fields and behaviors: CIK, ticker, company_name, LEI, FIGI, figi_candidates, unresolved, RxCUI, ingredient, brand, and a citation. It explains both input and output semantics per type, covers edge cases, and notes degradation, leaving little that an agent needs to infer.

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%, so the baseline is 3, but the description substantially enriches both parameters: it documents accepted input forms (ticker, CIK, ISIN, or company name) and gives specific examples ('ozempic', 'CH0038863350'). It adds crucial disambiguation rules for bonds, such as passing the issuer exactly as printed and never the full noun phrase.

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 example queries ('What's the ticker for…', 'find the CIK for…') and states the core operation: resolving a user-spoken NAME to the canonical/official identifiers other tools require as input. It identifies the exact resource (identifiers: CIK, LEI, FIGI, RxCUI) and supports two explicit entity types, 'company' and 'drug'.

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?

It explicitly instructs 'Use FIRST whenever you have a name but need an ID', giving a clear trigger condition. It also restricts the `value` parameter to the entity name only and warns against trailing security-class words, which orients the agent to the correct input discipline. It does not name sibling tools to avoid, but the 'first' directive plus the enrichment notes make the use case unambiguous.

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
Disambiguation3/5

Several tools overlap in purpose, especially the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) which all answer factual questions but differ in grounding/depth. The beta version is explicitly identical to the stable one currently, creating confusion. The Polymarket analysis tools also have overlapping scopes, though their descriptions help differentiate them. Overall, most tools have distinct roles but the heavy overlap in the query router cluster makes misselection a real risk.

Naming Consistency3/5

Tool names mix several conventions: bare verbs (remember, subscribe, forget), verb_noun (compare_entities, resolve_entity), noun_phrase (entity_profile, pipeworx_feedback), and a brand-prefixed family (ask_pipeworx*, polymarket_*). While some prefixes are consistent, the overall pattern is inconsistent and not predictable. Names are readable but do not follow a single style.

Tool Count2/5

With 33 tools, the server is heavily over-scoped for its name 'Unpaywall', which implies a narrow open-access search utility. Even though the actual functionality is broad, the tool count is excessive and will overwhelm agents. Many tools (llms_txt generation, dependency scanning, memory) are unrelated to the core data-query function.

Completeness4/5

For the actual domain revealed by the descriptions—a comprehensive data research and prediction-market gateway—the tool surface is quite complete: it covers discovery, retrieval, grounding, entity resolution, comparison, validation, subscriptions, memory, and prediction-market analysis. Minor gaps exist (e.g., no subscription update tool, no direct tool to list all data packs), but agents can work around them. If the domain is strictly 'Unpaywall/open access', it's severely incomplete, but the descriptions clearly indicate a broader scope.

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