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Speedrun

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

A4.6/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior, but the description adds substantial behavior beyond that: cascading through multiple lookup endpoints, graceful degradation if GLEIF/OpenFIGI is unavailable, returning figi_candidates on ambiguous matches, labeling identifier sources, and explicitly listing unresolved identifiers. No contradiction 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.

Conciseness3/5

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

The content is dense and nearly all informative, but the description is a long, single-paragraph block with heavy parenthetical asides and run-on clauses. While front-loaded with examples and the 'Use FIRST' directive, it could be significantly improved with structured bullets or clearer segmentation.

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 no output schema, the description explains what will be returned for both 'company' and 'drug' types, including fields like CIK, ticker, LEI, FIGI, RxCUI, unresolved identifiers, and source labels. It also covers edge cases, ownership information, and failure degradation, making it complete for an agent to understand and invoke 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?

While the schema already documents both parameters and has 100% coverage, the description adds valuable semantics: accepted input forms (ticker, CIK, ISIN, name), examples for both types, and a specific warning about passing only the entity name and not the full noun phrase for bonds. This materially helps an agent construct correct parameter values.

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+resource ('resolve a user-spoken NAME to the canonical/official identifiers') and gives multiple concrete query examples. It also differentiates itself by stating 'Use FIRST whenever you have a name but need an ID', which distinguishes it from sibling tools like entity_profile.

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 instructs when to use the tool: 'Use FIRST whenever you have a name but need an ID.' It provides rich context about accepted entity types and input forms, but it does not explicitly name alternative tools or state when not to use it, 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

A3.8/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is overlap in data querying tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, etc.) and prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). Detailed descriptions help differentiate them, but the number of similar-sounding tools increases the chance of misselection.

Naming Consistency3/5

Tool names show mixed conventions: some use verb_noun (e.g., find_user, list_subscriptions), others are noun_verb (e.g., entity_profile, bet_research), and there are prefixes like polymarket_ and pipeworx_. While subgroups are internally consistent, the overall set lacks a unified pattern.

Tool Count2/5

With 34 tools spanning speedrun.com queries, Pipeworx data access, memory management, subscriptions, and prediction markets, the server bundles multiple domains. The scope is too broad for a coherent single server; splitting into separate servers would improve usability.

Completeness4/5

Within each domain (speedrun.com, Pipeworx, Polymarket), the tool set covers key operations comprehensively, including research, arbitrage, fill risk, and monitoring. Minor gaps exist (e.g., no tool to place bets), but the overall surface is well-covered for the advertised functionalities.

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