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Udbud Dk

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

A4.8/5.0
Behavior5/5

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

Annotations already signal read-only, idempotent, and non-destructive behavior. The description adds substantial behavioral detail beyond that: internal cascading through multiple lookup endpoints, graceful degradation when GLEIF or OpenFIGI is unavailable, explicit handling of unresolved identifiers under an 'unresolved' field rather than omitting them, and clarification that non-equity instruments without tickers still resolve. No contradiction with annotations.

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?

Though long, the description is densely packed and every sentence earns its place: example queries, supported types, per-type identifier details, fallback behavior, and a final value-add statement about replacing manual lookups. It is organized with clear labels and front-loads the purpose before diving into specifics.

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 two-parameter entity-resolution tool with no output schema, the description provides complete calling context: accepted input forms, per-type outputs and sources, ownership enrichment details, unknown-identifier behavior, and system-unavailable fallbacks. An agent has everything needed to invoke it correctly and interpret results.

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 already 100%, with useful parameter descriptions. The description goes further by explaining exactly how to format 'value' for each type, giving examples (AAPL, 0000320193, ozempic), and warning against passing full noun phrases for bonds because trailing security-class words will match nothing. It also enriches the 'type' parameter by detailing what each enum value returns.

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-phrase examples and states the core operation: resolving a user-spoken NAME to canonical/official identifiers that other tools require as input. It is specific about supported types ('company', 'drug') and the identifiers returned (CIK, ticker, LEI, FIGI, RxCUI), making the purpose unmistakable even among a large sibling list.

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 establishes the intended context and even says it replaces 2-3 manual lookups. It does not explicitly name sibling alternatives or state when not to use it, but the use case is unambiguous enough to route an agent correctly.

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

A3.8/5.0
Disambiguation2/5

Several tools form near-overlapping clusters: ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior, while ask_pipeworx, ask_pipeworx_grounded, and deep_research have overlapping scopes. The five polymarket tools also share a common prediction-market domain and could be confused despite detailed descriptions. Some boundaries between 'meta' tools such as discover_tools, suggest_questions, and ask_pipeworx are also fuzzy.

Naming Consistency3/5

The majority of tools use lowercase snake_case and many follow a verb_noun pattern (ask_pipexors, search_within, generate_llms_txt, destroy_tools), which is readable. However, there are inconsistent orderings like ai_visibility_check and dk_tender_search, plus several one-word verb tools (remember, forget, recall) alongside noun_verb forms. So the style is mostly regular but does not follow a single consistent pattern.

Tool Count2/5

34 tools is very ot heavy for a general-purpose data platform, but the server name 'Udbud Dk' implies a narrow Danish tender scope. Only 3 of the 34 tools actually concern Danish procurement, while the rest form a broad question-answering, prediction-market, subscription, and memory platform. The count feels bloated and mismatched relative to the apparent server focus.

Completeness3/5

For the broad data domain, the server covers a wide range: lookup, grounded answers, entity resolution, fact-checking, company profiles, comparisons, change feeds, polymarket opportunities, subscriptions, and memory. But relative to the Danish tender scope implied by the server name, only search/detail/recent exist and missing functionality such as saved searches or notifications for new notices. There are also some odd gaps such as no-direct citation-lookup tool, but the generous question-answering tools compensate.

Resources