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Glama

Legislation Uk

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 signal read-only, open-world, idempotent, and non-destructive behavior. The description goes well beyond these by disclosing ambiguity handling (`figi_candidates` when a name matches multiple instruments), explicit `unresolved` reporting, graceful degradation when GLEIF/OpenFIGI are unavailable, and internal cascading through multiple lookup endpoints.

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 it is front-loaded with example phrases and the first-use heuristic, then organized into scannable type and behavior sections. Some sentences are dense, but given the tool's complexity and the absence of an output schema, the length is mostly justified.

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 no output schema, the description is exceptionally complete: it covers input formats, supported types, ambiguity behavior, unresolved identifiers, source provenance, fallback behavior, and why this tool is needed before other tools. An agent has everything required to invoke it correctly.

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 semantic detail: examples for ticker/CIK/name, bond issuer exact-print guidance, warning against trailing security-class words, ISIN-to-LEI behavior, and the meaning of candidate vs unresolved results. This materially improves the agent's ability to format inputs correctly.

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 clear verb-resource pair: resolve a user-spoken name to canonical/official identifiers required by other tools. It enumerates concrete example phrases and lists supported entity types, and the distinction from siblings is implicit through the 'other tools require as input' framing.

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 explicitly says 'Use FIRST whenever you have a name but need an ID,' which is strong when-to-use guidance. It does not name specific sibling tools as alternatives, but it clarifies that this tool replaces 2-3 manual lookups and covers cases other sources may miss, giving the agent enough context to select it.

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

The four legislation tools are clearly distinct, but the set as a whole is confusing: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and entity_profile, compare_entities, recent_changes, validate_claim, and ask_pipeworx_grounded have overlapping research/verification purposes. The 31 non-legislation tools also create constant ambiguity about which tool to pick for a UK law question.

Naming Consistency4/5

Names are uniformly lowercase snake_case and mostly follow recognizable verb-led or prefixed patterns (get_legislation, search_legislation, ask_pipeworx, polymarket_*). Minor deviations like entity_profile, bet_research, and recent_changes are noun-phrase style rather than verb_noun, but there is no camelCase mixing or chaotic convention.

Tool Count1/5

A server named 'Legislation Uk' has 35 tools, only 4 of which relate to UK legislation; the rest are Pipeworx data-platform, prediction-market, memory, and subscription utilities. This is an extreme mismatch between the tool count and the server's apparent purpose.

Completeness4/5

The UK legislation portion covers the core read workflow well: search_legislation finds a statute, get_legislation returns metadata, and get_legislation_text/get_legislation_section provide full or section-level text with versioning. Minor gaps exist, such as no dedicated amendment-history or cross-version diff tool, but there are no dead ends for typical lookups.

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