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Glama

dadjokes

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. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Even with readOnlyHint, idempotentHint, and destructiveHint annotations present, the description adds substantial behavioral context: it cascades through multiple lookup endpoints, degrades gracefully when GLEIF/OpenFIGI are unavailable, returns figi_candidates instead of asserting when a name is ambiguous, labels identifier provenance, and reports unresolved IDs explicitly rather than omitting them. This goes well beyond the annotation surface.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every clause earns its place: examples are front-loaded, SUPPORTED TYPES and caveats are visually scoped, and the graceful-degradation sentence conveys a behavior that would otherwise require a live call to discover. It reads dense rather than padded.

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?

For a no-output-schema tool with only two parameters, the description tells the agent what will come back (canonical IDs, source labels, figi_candidates, unresolved list, RxCUI/ingredient/brand/citation), how to handle ambiguous matches, what to avoid in value, and how failures degrade. No critical invocation information is missing.

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%, yet the description still adds meaning: it explains the type enum semantics for company and drug, gives accepted input forms for value, supplies concrete examples ('AAPL', '0000320193'), and warns that bond issuer names must be passed exactly as printed without trailing security-class words. That is true added value beyond the schema.

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 user phrases and a specific verb+resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It enumerates exactly what is resolved (CIK, LEI, ticker, RxCUI, FIGI) and explicitly frames itself as the identification step ahead of sibling profile/comparison tools, so it is readily distinguishable from entity_profile and 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?

'Use FIRST whenever you have a name but need an ID' is an explicit trigger condition and precedence rule, and the supported-types section clarifies when company vs drug resolution applies. It never names a sibling for the inverse case (e.g., already have an ID and want a profile), so it stops one step 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

A4/5.0
Disambiguation3/5

Most tools are clearly distinct, but the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are nearly identical, with the beta explicitly matching the stable version. Additionally, ai_visibility_check and scan_competitor_ai_presence overlap in purpose, creating some selection ambiguity.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (e.g., get_joke, search_jokes, resolve_entity, validate_claim). Even the more noun-like names like entity_profile and pipeworx_feedback fit the readable convention. No mixed camelCase or erratic verb usage.

Tool Count2/5

With 34 tools, the count is far above the typical well-scoped range of 3-15. The server is named 'dadjokes' but only 3 tools actually serve that purpose; the remaining 31 are an unrelated Pipeworx research and prediction-market platform, making the scope feel bloated and misaligned.

Completeness3/5

For the nominal dad-joke domain, the surface is complete (get, random, search). However, the intended domain is ambiguous given the massive unrelated toolset; it's unclear what a user should expect the server to cover, and the analysis/prediction tools lack obvious CRUD or management counterparts.

Resources