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Glama

Zenodo

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/openWorld/idempotent annotations, the description reveals non-obvious behavior: it cascades through several lookup endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, returns unresolved identifiers explicitly instead of omitting them, and returns figi_candidates when a name matches multiple instruments. 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 long and dense, but nearly every sentence adds operational value—edge cases, fallback behavior, supported instruments, and output labeling. It is front-loaded with examples and use cases. The main weakness is verbosity and nested parentheticals, which could be tightened without losing 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?

Given there is no output schema, the description carries the full burden of explaining what the tool returns: source-labelled identifiers, an unresolved list, figi_candidates for ambiguous matches, and a RxNorm citation URI. It also covers input constraints, unsupported/edge cases, and degradation behavior, making it sufficiently complete for correct invocation.

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?

Even though schema coverage is 100%, the description adds substantial meaning beyond the schema: it explains accepted input formats for each type, clarifies that ISIN resolves to the issuing legal entity, and warns to pass only the entity name with a concrete bond-issuer example. This materially reduces the chance of malformed 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 states a specific verb and resource: resolving user-spoken names to canonical/official identifiers, with concrete query examples like 'What's the ticker for…' and 'find the CIK for…'. It distinguishes the tool from siblings by positioning it as the first step whenever a name needs to become an ID, covering both 'company' and 'drug' entity types explicitly.

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 clear when-to-use guidance: 'Use FIRST whenever you have a name but need an ID.' It also enumerates supported inputs (ticker, CIK, ISIN, company name, drug brand/generic) and types. It does not explicitly name alternative sibling tools or state when not to use this tool, so it stops short of a full 5.

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

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, while ask_pipeworx, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap in the 'how should I query data' space. Additionally, the tool set mixes two unrelated domains (Zenodo and Pipeworx) without any organizing principle, forcing agents to guess which family applies.

Naming Consistency2/5

All names are lowercase snake_case, but the semantic patterns are inconsistent: bare nouns for Zenodo tools (search, record, communities), product-prefixed names (pipeworx_*, polymarket_*), generic verbs (remember, forget, recall), and verb_noun compounds (list_subscriptions, generate_llms_txt). The server is named Zenodo, yet most tools carry a pipeworx or polymarket prefix, making the naming feel arbitrary relative to the server's identity.

Tool Count2/5

36 tools is excessive for a server whose stated identity is Zenodo; only 5 of the 36 tools actually relate to Zenodo, with the remaining 31 belonging to a separate Pipeworx data platform. This suggests a bundled or mislabeled server rather than a deliberately scoped tool surface, and even the Zenodo subset alone would be thin.

Completeness2/5

For the Zenodo domain implied by the server name, the surface is severely incomplete: it covers search and read/retrieval (search, record, record_files, communities, community_records) but entirely omits the deposit workflow that is central to Zenodo — no create, update, delete, versioning, or file upload/download tools. The unrelated Pipeworx side is over-built, but the actual Zenodo use case leaves agents with dead ends.

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