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Tavily

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 mark the operation as read-only, open-world, idempotent, and non-destructive, so the description does not need to repeat those. It adds substantial behavioral context: graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` fields, `figi_candidates` on ambiguity, source-labelled identifiers, and internal cascading lookups. This greatly exceeds what annotations 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 dense and every sentence carries useful information, but it is a wall of text with long run-on clauses and nested parentheticals. The most important guidance is front-loaded with examples and 'Use FIRST', but readability could be improved with clearer separation of scenarios.

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 the tool's complexity, the lack of an output schema, and the two required parameters, the description is remarkably complete. It covers supported types, input formats, ambiguous-match behavior, unresolved identifier handling, degradation behavior, and the internal cascading nature of lookups. An agent has enough to call this tool correctly across edge cases.

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%, so the baseline is 3, but the description adds significant meaning beyond the schema: how `type` maps to supported sources (SEC EDGAR, GLEIF, OpenFIGI, RxNorm), how `value` is interpreted differently by type, and specific examples like ticker/CIK/ISIN/brand names. It also warns about bond issuer name formatting, which is crucial for 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 (named entity to canonical identifiers) and gives concrete example queries. It clearly differentiates from siblings by positioning itself as the first stop when a name needs to become an ID, which is distinct from entity_profile or compare_entities.

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?

Explicitly states 'Use FIRST whenever you have a name but need an ID.' It also provides detailed guidance on what inputs are valid, what entity types are supported, and even a negative example for bonds where trailing security-class words should be omitted. This is strong when-to-use 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
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same 5,529 tools, and deep_research overlaps for broad questions. The six prediction-market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also have heavily overlapping purposes, making misselection likely.

Naming Consistency4/5

All names use snake_case, which is consistent, and most are verb-first (search, extract, remember, resolve_entity, validate_claim). However, several are noun-phrases (entity_profile, polymarket_arbitrage, recent_alerts, pipeworx_feedback), breaking the verb_noun pattern. The deviations are minor but present.

Tool Count2/5

At 33 tools, the set is well above the 25-tool threshold for 'too many'. The server also spans several unrelated domains—web search, Pipeworx structured data, prediction markets, memory, subscriptions, AI visibility—making the count feel excessive for a coherent purpose.

Completeness4/5

Each sub-domain is well covered: search has search/extract/search_within, prediction markets have research/arbitrage/edges/fill-risk/tracking, subscriptions have subscribe/unsubscribe/list/recent_alerts, and memory has remember/recall/forget. Minor gaps exist (e.g., no way to edit a subscription's parameters, no direct SEC filing content viewer), but agents can work around them via ask_pipeworx.

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