Skip to main content
Glama

Plos

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?

Beyond the readOnly/idempotent annotations, the description reveals important behaviors: it cascades through multiple lookup endpoints internally, degrades gracefully when GLEIF/OpenFIGI is unavailable, returns figi_candidates when ambiguous rather than asserting a match, and reports unresolved identifiers explicitly under `unresolved`. This is substantial behavioral context that annotations alone do not convey.

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 but every sentence adds information, including examples, supported types, behavior on ambiguity, and degradation guarantees. It is longer than strictly minimal and could be better structured with separators, but it avoids fluff and front-loads the core purpose.

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?

With no output schema, the description carries the full burden of explaining what the caller gets, and it does: CIK, ticker, company_name, LEI with ownership, FIGI, RxCUI, ingredient, brand, `figi_candidates` for ambiguous cases, and `unresolved` for failures. Graceful degradation and internal cascading are also covered, leaving no major gap for an agent deciding how to call and interpret this 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%, but the description adds critical semantics beyond the schema: it expands the accepted company input to include ISIN, explains that value should be the ENTITY NAME ONLY and not a full noun phrase, and gives concrete examples like 'NEW YORK ST DORM AUTH' versus trailing security-class words. 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 specific verb—'resolve a user-spoken NAME to the canonical/official identifiers'—and concretely scopes the tool to company and drug entity types. It also frames the tool as the first stop when a name is known but an ID is needed, which clearly differentiates it from siblings like entity_profile or 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 gives explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains what types of names are acceptable and when ambiguity should be resolved. It does not explicitly name sibling tools as alternatives or state when not to use it, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Plos toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.2/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical in function, while compare_entities and entity_profile both fan out across data sources. The PLOS-specific tools (article, search, recent) are distinct, but the broader set creates confusion about which entry point to use for general data questions.

Naming Consistency2/5

The naming is a mix of single-word nouns (article, recent), verb phrases (search_authored_by, validate_claim), and adjective_noun constructions (recent_changes, recent_alerts). While most multi-word names use snake_case, the lack of a consistent prefix or verb_pattern (e.g., some start with action verbs, others with data categories like polymarket_ or pipeworx_) makes the set feel inconsistent.

Tool Count2/5

At 35 tools, the server is heavily over-scoped for a PLOS journal interface—only 4 tools (article, search, search_authored_by, recent) actually relate to PLOS. The remaining 31 tools form a broad Pipeworx/Polymarket data platform, which suggests the server is trying to do far more than its name implies.

Completeness2/5

For the stated domain (PLOS), the surface is thin: basic search, fetch, recent, and author search lack advanced features like citation metrics, journal browsing, or full-text download links. Conversely, the Pipeworx tools are extensive but unrelated to PLOS, so the set is simultaneously over-complete in unrelated areas and incomplete for its apparent purpose.

Resources