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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?

The description goes well beyond the readOnly and idempotent annotations by disclosing cross-source composition, cascading lookups, graceful degradation when GLEIF or OpenFIGI is unavailable, ambiguity handling via figi_candidates, and explicit unresolved identifier reporting. It also warns about a real input pitfall for bond issuer names. No contradiction with the 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 front-loaded with trigger phrases and use-first guidance, and it is organized under SUPPORTED TYPES. It is long and dense, but nearly every clause contributes a behavioral constraint or example, so the length is justified; slight trimming of parenthetical detail could make it more scannable.

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?

Even without an output schema, the description covers input variants, ambiguous matches, unresolved identifiers, enrichment degradation, and why the tool replaces multiple manual lookups. An agent has the necessary information to select and call the tool 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?

Although the schema covers both parameters, the description adds substantial meaning: accepted alias formats like ticker, CIK, ISIN, and company name; ISIN-to-legal-entity resolution behavior; drug brand/generic examples; and the explicit instruction to pass only the entity name, with a bond issuer example, because trailing security-class words break FIGI matching.

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 natural-language triggers for name-to-ID lookups and states exactly what it resolves: a user-spoken name to canonical official identifiers such as CIK, ticker, LEI, FIGI, and RxCUI. It differentiates from siblings by saying it supplies the identifiers other tools require as input and should be used first when a name is known but an ID is needed.

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?

It explicitly says to use this tool first whenever you have a name but need an ID, and it enumerates supported entity types and accepted input forms. It does not name alternative tools or state when not to use it, so it lacks the explicit exclusion that would earn a 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

A3.8/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly described as functionally identical right now, making them near-duplicates. ai_visibility_check and scan_competitor_ai_presence also heavily overlap, and deep_research vs ask_pipeworx requires careful reading to know which to pick.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern (list_templates, create_*, validate_claim). However, a few bare verbs break the pattern: compose, forget, recall, and remember.

Tool Count1/5

34 tools is already heavy, but the server is named 'Gitignore' and only 3 of 34 tools relate to .gitignore templates. The other 31 tools form a completely unrelated data platform, making the count an extreme mismatch for the apparent scope.

Completeness3/5

As a data platform, the surface is broad but has notable gaps: citations return pipeworx:// URIs yet no fetch/read-by-URI tool exists, and subscriptions support subscribe/unsubscribe/list but not update. For the gitignore name, only basic template list/get/compose is present, with the rest irrelevant to the stated purpose.

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