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Apis Guru

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 readOnly/idempotent/non-destructive, and the description adds substantial behavioral detail beyond that: ambiguous matches 'assert nothing' and return figi_candidates, unresolved identifiers are listed explicitly, enrichment degrades gracefully, and each call cascades through multiple endpoints. This is rich and useful context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

Although long, the description is highly organized and front-loaded: trigger phrases and the core 'Use FIRST' rule come first, followed by structured supported-type sections. Every sentence contributes meaningful operational detail; the length is justified by the tool's complexity.

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 compensates thoroughly: it explains return semantics (labelled identifiers, unresolved list, figi_candidates), input variations, enrichment failure behavior, and internal cascade behavior. For a complex tool with two entity types, this is complete enough for an agent to call 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 meaning well beyond the schema: it documents ISIN as an additional accepted input for company, explains exactly how to format the value ('Pass the ENTITY NAME ONLY'), gives a bond-issuer edge-case, and provides concrete examples for both types.

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 uses a specific verb ('resolve') with a clear resource: user-spoken names to canonical/official identifiers. It distinguishes itself from siblings by framing itself as the prerequisite step ('identifiers other tools require as input') and by listing concrete trigger phrases and supported entity types.

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 gives explicit usage guidance: 'Use FIRST whenever you have a name but need an ID', with multiple example phrasings and clear supported types. It does not explicitly name sibling alternatives or state 'do not use when the ID is already known', but the conditions are strongly implied.

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

A4/5.0
Disambiguation3/5

Several tools have overlapping purposes—ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-twins (beta currently matches the stable router exactly), and deep_research, entity_profile, recent_changes, and compare_entities all fan out across similar data sources. The long descriptions do help differentiate them, but an agent selecting quickly could easily pick the wrong variant.

Naming Consistency3/5

Most tools use snake_case, but the verb style is inconsistent: get_api, list_providers, and validate_claim use verb_noun, while remember/forget/recall are bare verbs and polymarket_arbitrage, entity_profile, and bet_research are noun phrases. The pattern is readable but not predictable enough to infer behavior from the name alone.

Tool Count2/5

At 35 tools, the surface is heavy, and many are hyper-specialized (five separate Polymarket tools, three ask_pipeworx variants, three memory tools). The breadth is defensible for a multi-domain data platform, but it goes past the comfortable 16-25 range and would benefit from consolidation.

Completeness4/5

The tool set covers the main research lifecycle well: discovery, routing, grounded answers, entity resolution, profiling, comparison, claim validation, change tracking, subscription management, and memory. Minor gaps exist—such as no explicit fetch-by-citation-URI tool and soft-failed patent coverage—but agents can generally work around them.

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