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Glama

Biorxiv

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?

Annotations already declare readOnly/openWorld/idempotent/non-destructive, lowering the bar. The description still adds substantial behavior: cross-source identity spine, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` fields rather than omission, `figi_candidates` when a name is ambiguous, and source-labeling of identifiers. This is rich, non-obvious behavior that goes well beyond the 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 and dense, but nearly every clause carries useful information and it is front-loaded with examples and a clear usage directive. It is less tight than ideal, with some rambling clauses, but it is organized into recognizable sections (examples, usage, supported types, degradation) and has no filler.

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?

There is no output schema, so the description must carry the burden of explaining return semantics. It does this well for the key cases: CIK, ticker, LEI, FIGI, RxCUI, `unresolved`, `figi_candidates`, and source labels. It does not provide a complete output shape, but for a tool this complex and with only two simple parameters, the description is sufficiently complete for an agent to call it correctly.

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 coverage is 100%, so baseline is 3. The description adds valuable parameter semantics beyond the schema, especially the warning to pass the entity name only and not the full noun phrase, with a concrete bond-issuer example. It also clarifies how `type` interacts with lookup behavior, which meaningfully helps an agent construct correct calls.

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 natural-language triggers and states the core purpose: resolving a user-spoken name to canonical identifiers other tools require as input. It distinguishes the tool from siblings by naming what it replaces (2-3 manual lookups) and by covering multiple entity types, so an agent can identify when this is the right tool.

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 clearly implies when to call this tool, but it does not explicitly state when not to use it or name alternative sibling tools it should be preferred over. That leaves some ambiguity, but the trigger condition is strong.

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

B3.1/5.0
Disambiguation2/5

Many tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve querying/research, while entity_profile, compare_entities, and recent_changes overlap on company information. The bioRxiv-specific tools are distinct but are swamped by generic Pipeworx tools, making selection ambiguous.

Naming Consistency2/5

Though all names are snake_case, there is no consistent verb-noun pattern. Some tools are verbs (remember, recall, forget), some are nouns (details, summary, publisher), and the ask_pipeworx family and meta-tools like discover_tools, suggest_questions mix styles. The naming feels ad hoc rather than following a clear convention.

Tool Count2/5

35 tools is excessive for a server named 'Biorxiv' — only 4-5 tools relate to bioRxiv directly, while the rest are general-purpose Pipeworx data and monitoring tools. This is a severe mismatch between server name and scope, bloating the tool surface unnecessarily.

Completeness2/5

For the stated bioRxiv purpose, the surface is incomplete: there is no search-by-topic, author, or abstract, and no way to retrieve full preprint text. The server compensates with many unrelated tools, but the core bioRxiv domain lacks basic coverage like searching preprints.

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