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tle

by io.github.pipeworx-io

Validate Claim

validate_claim
Read-onlyIdempotent

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pctNoMax percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

TDQS

A4.6/5.0
Behavior5/5

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

Despite having readOnlyHint, openWorldHint, idempotentHint, and destructiveHint annotations, the description adds substantial behavioral context: the existence of two distinct pipelines (SEC EDGAR+ XBRL fast path vs grounded), the verdict enum, the meaning of could_not_verify as a non-evidence error with verification_error, and unsupported as a coverage gap. This goes well beyond 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 dense but well-structured, with a clear intro via natural-language triggers, usage guidance, pipeline breakdown, return format, and important warnings. It is longer than the 2-sentence ideal but every sentence conveys necessary information, and the 'IMPORTANT for callers' section is appropriately highlighted. No wasted words, though it could be tightened slightly.

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 description is remarkably complete: it explains return values (verdict, actual value, citation, reasoning), distinguishes critical outcomes (could_not_verify vs unsupported), and specifies pipeline routing. There is no output schema, so this description carries the full burden, and it does so thoroughly.

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?

The input schema covers both parameters with descriptions (100% coverage), so baseline is 3. The tool description adds value by explaining that tolerance_pct overrides the implied tolerance, gives a specific use case (set 1–2 for hallucination detection), and clarifies the default cap of 5. This is beyond schema but not exhaustive.

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 clearly identifies the tool as natural-language claim verification against authoritative sources, with explicit phrasing examples and the intended function of returning a verdict. It distinguishes from siblings by emphasizing the fact-checking role with verdicts and pipeline details (SEC EDGAR vs grounded), and notes it replaces 4–6 sequential calls.

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 states 'Use whenever the agent needs to check whether something a user said is factually correct,' giving a clear trigger. It also provides nuanced guidance for company-financial claims versus other claims, and explains the meaning of could_not_verify and unsupported. However, it does not explicitly name alternative tools or explicit when-not-to-use scenarios, leaving room for refinement.

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.

Resources