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Glama

Dataverse Harvard

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?

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, so the bar is lower. The description adds substantial context: cross-source identity spine, source-labelled identifiers, explicit `unresolved` field, `figi_candidates` ambiguity behavior, ISIN-to-LEI legal entity resolution, and graceful degradation. This 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, but almost every clause earns its place given the tool's multi-source complexity and no output schema. It is front-loaded with trigger examples and the 'Use FIRST' guidance, though it is a dense single paragraph that could benefit from clearer structure or bullet separation.

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?

There is no output schema, so the description carries full burden, and it delivers: identifier sources, resolution behavior for ambiguous matches, unresolved-identifier behavior, accepted input forms, degradation of enrichment, and entity-specific return fields. An agent has enough to select and invoke this tool correctly in almost all cases.

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 meaning beyond the schema: accepted input forms for `value` include ISIN even though the schema paragraph omits it, and it gives detailed guidance on passing exactly the issuer name ('NEW YORK ST DORM AUTH') versus the full noun phrase. It also clarifies what `type` 'company' vs 'drug' return and how FIGI matching fails with trailing security-class words.

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 names a precise verb-resource pair: resolving a user-spoken name to canonical official identifiers required by other tools. It gives concrete trigger-phrase examples and enumerates supported entity types (company, drug), clearly separating itself from search or validation tools via the 'Use FIRST' directive.

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 says 'Use FIRST whenever you have a name but need an ID,' which provides strong usage context. It also explains when ambiguity should be left unresolved and when enrichment degrades gracefully, though it does not name sibling tools or state explicit when-not-to-use conditions beyond the name-to-ID scope.

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

B3.4/5.0
Disambiguation3/5

Several tool pairs have overlapping purposes, notably ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded. Also, remember/recall/forget overlap with general memory operations, and multiple polymarket tools overlap in edge detection. While the descriptions attempt to differentiate, an agent will frequently need to choose between nearly identical tools (e.g., ask_pipeworx vs. ask_pipeworx_beta).

Naming Consistency2/5

Naming conventions are mixed: snake_case (ai_visibility_check, compare_entities), camelCase (ask_pipeworx, generate_llms_txt), and inconsistent verb usage (some start with verbs like 'search', others with nouns like 'dataset'). The polymarket and pipeworx prefixes are helpful, but overall patterns are unpredictable.

Tool Count2/5

With 35 tools, this server has a very large surface area. While the domain is broad (Harvard Dataverse + Pipeworx data + Polymarket), the count feels heavy and includes many near-duplicate tools (ask_pipeworx variants) and niche tools that inflate the total. Many agents would benefit from a smaller, more focused set.

Completeness3/5

The Dataverse subset captures file metadata and search but lacks direct download/upload capabilities, causing dead ends for users who want to access actual data. The Polymarket subset lacks the ability to actually place orders despite extensive edge analysis. The Pipeworx subset covers many data queries but feels unfocused. Overall, there are notable gaps given the stated scope of the server.

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