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Securitytrails

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.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent, and the description adds substantial behavioral detail: multi-source cascades through EDGAR, GLEIF, OpenFIGI, and RxNorm; ambiguous matches return `figi_candidates`; unresolved identifiers are stated explicitly; enrichment degrades gracefully. This is far beyond what annotations alone provide.

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 it is front-loaded with examples and every sentence adds functional value for a complex multi-source resolver. Some subclauses are run-on and would benefit from clearer headings, 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 still explains what to expect: labelled identifiers, source provenance, `unresolved` fields, ambiguity via `figi_candidates`, and graceful degradation. It also tells the agent that one call replaces multiple manual lookups, which is important for planning.

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 schema coverage is 100%, the description adds critical meaning beyond the schema. It specifies acceptable value forms (ticker, CIK, ISIN, brand/generic), explains that a bond must use the issuer name exactly as printed, and describes how ISIN maps to legal entity. 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 opens with a specific verb+resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It immediately distinguishes the tool from profile, compare, and validation siblings by focusing on ID resolution and giving many concrete query examples.

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?

The description explicitly says 'Use FIRST whenever you have a name but need an ID,' giving strong when-to-use guidance. It also gives type-specific value constraints and warns against passing the full noun phrase. It does not name alternative sibling tools or explicit exclusion cases, but the context 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

A3.8/5.0
Disambiguation2/5

Multiple tools occupy the same general query/research space: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, validate_claim, entity_profile, and recent_changes all overlap in what they can return. The descriptions are detailed and try to steer usage, but the boundaries are fuzzy enough that agents can easily select the wrong tool.

Naming Consistency3/5

All names are snake_case and descriptive, but there is no consistent verb_noun convention across the set. It mixes bare verbs (remember, recall, forget), noun phrases (entity_profile, recent_changes), domain-prefixed families (securitytrails_*, polymarket_*), and Pipeworx meta-tools (ask_pipeworx_*), so the pattern is predictable only within each family.

Tool Count2/5

35 tools is above the 25+ threshold for a coherent MCP surface, and many are highly specialized (Polymarket arbitrage, AI visibility checks, npm dependency scans) rather than core Securitytrails functionality. The set feels like multiple products merged into one rather than a well-scoped toolset.

Completeness3/5

The broad data-research workflows are well covered: routing, entity resolution, profiling, comparison, validation, subscriptions, memory, and basic Securitytrails domain lookups. But for a server named Securitytrails, there are obvious missing security-intelligence operations such as associated domains, IP/certificate enrichment, and broader DNS infrastructure enumeration.

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