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Glama

Hex Pm

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 declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the bar for adding value is higher. The description adds substantial context beyond annotations: graceful degradation when GLEIF/OpenFIGI is unavailable, explicit `unresolved` entries rather than silent omission, source-labelled identifiers, and `figi_candidates` when ambiguity exists. No contradiction with annotations is present.

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 and dense, but the length is justified by the tool's complexity and the need to distinguish two entity types with different resolver behavior. It front-loads example queries and primary purpose before diving into edge cases. Slightly more formatting—such as bulleted sub-sections—would improve readability, but it is not wasted text.

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?

Given no output schema, the description carries the full burden of explaining what the tool returns and how it behaves. It covers company and drug types, identifier sources, ambiguous matches, unresolved identifiers, fallback behavior, and input format constraints. An agent has enough context to select and call this tool correctly in almost all cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3; the description compensates with valuable guidance beyond the schema. It clarifies accepted inputs (ticker, CIK, ISIN, name) and adds a critical caveat: pass only the entity name, not trailing security-class words, because FIGI lookup matches instrument names. This materially improves 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 states a specific verb and resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It immediately lists supported entity types (company, drug) and distinguishes the tool's role from siblings like entity_profile or compare_entities by focusing on identifier resolution rather than profiles or comparisons.

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?

It explicitly says 'Use FIRST whenever you have a name but need an ID,' giving direct routing guidance. It also provides nuanced when-to-use details, such as handling non-equity instruments without tickers, ISIN-to-LEI resolution for non-US issuers, and what happens when a name matches multiple instruments.

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 occupy overlapping question-answering territory: ask_pipeworx_beta currently behaves identically to ask_pipeworx, while ask_pipeworx_grounded, deep_research, and validate_claim all route the same data catalog and differ mainly in output guarantees. The six Polymarket tools also split edge detection, arbitrage, and fill-risk in ways that are easy for an agent to conflate. Clear exceptions like the memory and subscription trios keep it from a 1.

Naming Consistency4/5

Names are uniformly lowercase snake_case and most follow a readable verb-first or domain-prefixed pattern (get_package, list_releases, scan_dependency, ask_pipeworx_*). The Polymarket family uses noun phrases after a prefix (polymarket_edges, polymarket_fill_risk) and a few names are noun-first (entity_profile, recent_changes), which is a minor inconsistency rather than chaos.

Tool Count2/5

35 tools is well beyond the comfortable 3-15 range and even above the 16-25 heavy range. The broad Pipeworx data scope justifies some expansion, but identical ask_pipeworx_beta, six overlapping Polymarket tools, and unrelated utility families (Hex.pm, AI visibility, memory, llms.txt) suggest bloat rather than deliberate scoping.

Completeness4/5

Within its main data-access purpose, the surface is unusually complete: query (ask_pipeworx), grounded verification (ask_pipeworx_grounded/validate_claim), deep research, entity resolution/profiling, comparison, change feeds, and search-within are all present, and memory/subscription subdomains have full CRUD. There are minor gaps for the package side (no docs/dependents) and the hodgepodge of domains makes a single 'complete' surface hard to define, but no workflow hits a hard dead end.

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