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postcodes

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

A4.7/5.0
Behavior5/5

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

Goes far beyond the readOnly/idempotent annotations by describing ambiguity handling (returns figi_candidates rather than asserting), explicit unresolved identifiers, graceful degradation of LEI/FIGI enrichment, cascading internal lookups, and non-equity instrument coverage. It is fully consistent 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.

Conciseness4/5

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

The description is long but front-loaded with user intent and clearly structured by SUPPORTED TYPES and enrichment notes. Each clause carries behavioral or usage information; dense parentheticals prevent a perfect conciseness score, but there is no filler.

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?

With no output schema, the description carries the return-value burden. It enumerates returned identifiers (CIK, ticker, LEI, ownership, FIGI candidates, unresolved, RxCUI, ingredient, brand, citation), labels data sources, and explains degradation and ambiguity behavior. An agent has enough context to invoke and interpret results 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%, and the description adds substantial value beyond the schema: accepted input formats per entity type, the exact-passing rule for bond issuer names, why trailing security-class words fail, ISIN-to-LEI behavior, and brand/generic examples. This meaningfully reduces the chance of misusing the 'value' parameter.

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 specific verb+resource: it resolves user-spoken names to canonical/official identifiers for company and drug entity types. It also explicitly positions itself as the entry point when you have a name but need an ID, distinguishing it from sibling lookup/research tools.

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?

Provides explicit when-to-use guidance via 'Use FIRST whenever you have a name but need an ID' and rich trigger examples. It does not explicitly state when not to use it or name alternative sibling tools, but the scope boundary ('name → ID') is clear enough.

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
Disambiguation2/5

Many tools have overlapping responsibilities: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all serve similar lookup purposes. entity_profile, compare_entities, and recent_changes all retrieve company data. Several Polymarket tools overlap in edge detection. The large number of tools with fuzzy boundaries makes it difficult for an agent to select the correct one.

Naming Consistency3/5

Tool names are a mix of conventions: some use verb_noun (lookup_postcode, validate_postcode, resolve_entity), others are verb_phrase (ask_pipeworx, deep_research, suggest_questions), and a few are compound (polymarket_arbitrage, scan_competitor_ai_presence). No uniform pattern, though the structure is readable.

Tool Count1/5

Despite being named 'postcodes', only 4 of 35 tools are directly about postcodes. The vast majority belong to a broad data platform (Pipeworx) with specialized tools for finance, betting, news, etc. The count is excessive for a focused service, and many tools are only useful for users of that platform, leading to clutter.

Completeness2/5

For a postcode server, the tools cover basic needs (lookup, nearest, random, validate). However, the server's actual scope is much larger; within that broader scope, there are notable gaps: no general text search, no direct access to raw SEC filings, and many tools depend on paid plans or external accounts. The coverage is uneven and incomplete for a unified data platform.

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