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Fara

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 mark it read-only/idempotent/non-destructive, and the description adds extensive behavioral context: source-specific fields returned (EDGAR, GLEIF, OpenFIGI, RxNorm), conditional parent/children ownership, graceful degradation if GLEIF/OpenFIGI is unavailable, and internal cascade behavior. No statement contradicts 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 examples and the 'Use FIRST' directive, and nearly every clause adds useful information. However, it is written as one dense run-on paragraph with heavy parentheticals and jargon ('cross-source identity spine'), so it is less skimmable than it could 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?

There is no output schema, yet the description explains what each type returns, includes edge cases (non-equity instruments, ISINs, non-US issuers), and notes fallback behavior when enrichment sources are unavailable. An agent has enough information to select the right type and value and anticipate the result.

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 already describes both parameters (100% coverage), the description goes well beyond it with concrete examples, valid value formats, and an explicit warning to pass the entity name only rather than the full noun phrase, including the bond issuer edge case. This materially improves the agent's chance of forming a correct value.

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 user phrasings and states the core action: resolve a user-spoken name to canonical/official identifiers required by other tools. It further differentiates by supported types (company/drug) and declares 'Use FIRST whenever you have a name but need an ID,' so an agent can recognize when this tool is the intended lookup.

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 an explicit trigger condition ('Use FIRST whenever you have a name but need an ID') and explains that one call replaces 2-3 manual lookups. It does not name specific alternative tools or state when NOT to use resolve_entity, so it stops short of full exclusion guidance.

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

There are several clusters of tools with overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all do data-fetching/research with somewhat subtle differences. Polymarket tools also overlap (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, bet_research, polymarket_kalshi_spread). However, most tools have detailed descriptions that clarify their distinct roles, and the core data-lookup tools are differentiated by grounding level and scope.

Naming Consistency4/5

Most tools follow a consistent verb_noun or noun_verb pattern (e.g., search_registrants, list_foreign_principals, get_registrant_documents, subscribe, unsubscribe, remember, recall, forget, resolve_entity, validate_claim). Deviations include brand-name tools like ask_pipeworx, pipeworx_feedback, pipeworx_trending, and polymarket_kalshi_spread that mix conventions but are still readable and predictable within their domain.

Tool Count2/5

34 tools is heavy for a single MCP server, especially with multiple overlapping research entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions). The broad data-router nature of the server explains the size, but it is still a large surface that would be better consolidated.

Completeness4/5

The server covers its visible domains well: data lookup, grounded verification, entity profiling, comparison, change tracking, subscription lifecycle, memory, and FARA-specific queries. Minor gaps exist (e.g., no direct tool for updating saved memory beyond forgetting/re-remembering, no tool to create custom alert types beyond the three supported categories), but the core workflows are complete.

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