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Glama

Uk Contracts

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is handled. The description goes well beyond those: it discloses graceful degradation of LEI/FIGI enrichment, explicit `unresolved` field for failed lookups, the cascade through multiple endpoints, and the behavior of returning `figi_candidates` when a name matches several instruments. These are behavioral specifics that an agent needs to set expectations correctly, all beyond what annotations provide.

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 information-dense. It is front-loaded with the most actionable trigger examples, then systematically covers supported types, input formats, edge cases, and fallback behavior. A few phrases could be trimmed (e.g., the parenthetical on 'non-equity instruments'), but overall every sentence contributes to accurate use. The structure (examples → types → field semantics → degradation) is logical, though it runs on in places.

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 tool with only two parameters and no output schema, the description covers every practical need: input formats, supported entity types, resolution sources, ambiguity handling, unresolved results, and graceful degradation. It even notes that ISINs resolve to the issuing legal entity via GLEIF. Nothing an agent needs to call this correctly and interpret results is missing, especially given the tool's complexity and the breadth of identifiers it returns.

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% (both parameters have descriptions), so per the rubric the baseline is 3. But the description adds substantial nuance: for `value`, it instructs to pass the entity name only, gives the example 'NEW YORK ST DORM AUTH' versus 'revenue bonds', and explains why trailing security-class words break the FIGI lookup. For `type`, it enumerates accepted inputs per type (ticker, CIK, ISIN, etc.). These details are not in the schema and materially improve correct invocation.

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 query examples and states the core purpose: resolving a spoken name to canonical identifiers. It explicitly lists supported types ('company', 'drug') and the identifier families each returns (CIK/ticker, LEI, FIGI, RxCUI). It clearly distinguishes this from other tools by positioning it as the 'FIRST' step when a name needs an ID, and it picks out the non-equity resolution niche that sibling tools might not cover.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit direction: 'Use FIRST whenever you have a name but need an ID.' It provides trigger phrases ('What's the ticker for…') and details on when to use company vs drug. It also explains that it replaces 2-3 manual lookups and declines to assert on ambiguous matches, returning candidates instead — which is exactly the correct usage for an issuer name that doesn't map to a single bond. No explicit alternatives are named, but the use case is so crisply defined that exclusion is implicit.

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

A3.9/5.0
Disambiguation2/5

Several tools are near-identical entry points: ask_pipeworx_beta explicitly matches ask_pipeworx exactly, ask_pipeworx_grounded only differs in grounded extraction, and deep_research overlaps with both. Company-research tools (entity_profile, compare_entities, recent_changes, validate_claim) and the Polymarket scanner family also have fuzzy boundaries despite long descriptions.

Naming Consistency3/5

All names are lowercase snake_case, which is a consistent base convention. However, the pattern mixes verb-first names (search_notices, get_notice, list_subscriptions), noun-first names (entity_profile, polymarket_edges, pipeworx_trending), and bare verbs (remember, recall, forget).

Tool Count2/5

At 36 tools, the server is well past the 25-tool threshold and feels like a platform dump rather than a scoped toolkit. Only about five tools actually serve the 'UK Contracts' name; the rest are general Pipeworx routing, Polymarket betting, memory, subscription, and AI-visibility utilities.

Completeness4/5

The UK procurement surface itself is solid: search_notices, recent_notices, and get_notice cover Contracts Finder, while find_a_tender_recent and find_a_tender_notice cover high-value Find a Tender notices. Minor gaps exist—notably no full-corpus keyword search for high-value Find a Tender notices, and Contracts Finder detail does not list documents—but agents can usually work around these.

Resources