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tle

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, and the description adds substantial behavioral context: ambiguous names return figi_candidates rather than asserting, unresolved identifiers are listed explicitly, identifiers are labeled by source, and LEI/FIGI enrichment degrades gracefully. This goes well beyond what the annotations convey, with no contradiction.

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?

Purpose and usage are front-loaded, which is good, but the company bullet is a dense run-on with multiple nested parentheticals that strain parseability. Every detail is informative, yet the formatting makes the definition harder to process than it needs to 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?

Even without an output schema, the description tells the agent what will be returned (CIK, ticker, company_name, LEI, FIGI, RxCUI, ingredient, brand, citation, unresolved, figi_candidates) and covers edge cases: ambiguous matches, unavailable external sources, non-US issuers, and non-equity instruments. Input expectations for both parameters are thoroughly specified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the value parameter already includes examples and warnings. The description adds extra meaning by enumerating accepted input forms (ticker, CIK, ISIN, name), explaining ISIN-to-LEI behavior, non-equity instrument support, and the disambiguation behavior. This exceeds the schema's baseline contribution.

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 ('user-spoken NAME to canonical/official identifiers'), and immediately positions the tool as the first stop when a name needs an ID. The supported types and examples make it easy to distinguish from siblings like entity_profile or compare_entities, which focus on profiling or comparison rather than resolution.

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,' reinforced with concrete example phrasings. However, it does not name sibling alternatives or state exclusions, so the agent still has to infer when entity_profile or compare_entities would be more appropriate.

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

Most tools have clearly distinct jobs, but ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research are overlapping query/research entry points—and ask_pipeworx_beta is currently identical to ask_pipeworx. Similarly, ai_visibility_check and scan_competitor_ai_presence overlap by composition. The descriptions are detailed enough to choose correctly with care, but the boundaries are not always crisp.

Naming Consistency3/5

All names use lowercase snake_case, which keeps the surface readable, but the naming conventions are mixed: some are imperative verbs (get_tle, list_recent, validate_claim), some are noun phrases (entity_profile, recent_alerts, pipeworx_trending), and several use domain prefixes without a clear verb (polymarket_arbitrage, polymarket_edges). This is still discoverable naming, but it does not follow a consistent verb_noun pattern.

Tool Count2/5

34 tools is well beyond the well-scoped range, and the vast majority belong to a broad Pipeworx research/prediction-market platform rather than the server's apparent 'tle' satellite theme. Only get_tle, list_recent, and search_satellites directly match the server name. It feels like several tool surfaces aggregated into one server rather than one coherent product.

Completeness3/5

For the satellite TLE theme, NORAD lookup, name search, and recent-catalog listing are covered, but orbit propagation, pass prediction, and historical TLE data are missing. For the broader data-research surface, coverage is rich—query, grounded answers, entity resolution, comparison, validation, subscriptions, and memory are all present—so agents have workable paths for most tasks, but the server's mixed scope creates obvious thematic gaps.

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