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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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent hints, and the description adds substantial behavioral context beyond that: graceful degradation when GLEIF/OpenFIGI is unavailable, explicit `unresolved` reporting, ambiguity behavior with `figi_candidates`, source-labelled identifiers, and internal endpoint cascading. No contradictions 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 dense and front-loaded with the core directive and supported types. Nearly every sentence earns its place given the tool's complexity; the main deduction is that the single-paragraph structure makes it harder to scan than a more segmented write-up would be.

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?

Despite having no output schema, the description tells the agent what to expect (identifiers, `unresolved`, `figi_candidates`, RxCUI citations) and covers edge cases like non-ticker instruments, non-US issuers, and enrichment failure. The explanation is sufficient for correct selection and invocation.

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 crucial meaning beyond the schema: examples for `value` (AAPL, 0000320193, ozempic), a warning to pass only the entity name and not the full noun phrase, and the ISIN-to-LEI semantics for company lookups. 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 ('resolve') and resource ('user-spoken NAME to canonical/official identifiers'), and immediately grounds it with concrete user phrasings. It also differentiates the tool from likely siblings by describing its role as the identity-lookup layer that feeds other tools.

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?

Provides explicit guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains the types of entities handled (company, drug), supplies accepted input formats, and notes that it replaces 2-3 manual lookups, making the selection condition unmistakable.

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
Disambiguation3/5

Several tools share the same basic purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to data sources, and ask_pipeworx_beta is currently identical to ask_pipeworx. The polymarket_* family has five overlapping tools (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread), though detailed descriptions and explicit 'use when' guidance help separate them. Overall, an agent can generally pick the right tool but faces real ambiguity in the query-router and betting clusters.

Naming Consistency4/5

Most tools follow a clear verb_noun snake_case convention (search, get_contents, resolve_entity, validate_claim, subscribe, unsubscribe). However, several noun-first names break the pattern: entity_profile, ai_visibility_check, pipeworx_feedback, pipeworx_trending, and the polymarket_* family, plus adjective-noun names like recent_alerts and recent_changes. The deviations are readable and mostly clustered around product-specific domains, so the inconsistency is minor.

Tool Count2/5

At 34 tools, this significantly exceeds the 25+ threshold for a heavy tool surface. The server bundles four distinct domains — web search, structured data routing, prediction-market analysis, and memory/subscriptions — into one MCP endpoint, which inflates the count. While each domain has some justification, a more focused split into separate servers would yield better coherence.

Completeness4/5

The surface is remarkably thorough for its blended scope: search has query/retrieve/similar/within, structured data has default/grounded/beta/deep-research modes, subscriptions have full lifecycle coverage, and memory has save/recall/delete. Minor gaps exist, such as no subscription-update tool and no direct pipeworx:// URI reader in the tool list, but these are workable. The prediction-market and entity-analysis workflows are covered end to end.

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