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Glama

Useragent

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

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false), the description adds substantial context: the two processing paths, the verdict enum values, the critical distinction between 'could_not_verify' and 'unsupported', and the meaning of tolerance_pct. It explains failure modes and citation format, all consistent with 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.

Conciseness5/5

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

The description is long but densely informative. It opens with natural-language examples, then states when to use it, explains the internal routing, enumerates the verdict values, and highlights a critical caller-facing caveat. The 'IMPORTANT for callers' section is justified and adds critical safety information without redundancy.

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 has no output schema, the description fully explains what is returned (verdict, actual value with citation, reasoning) and covers error semantics (could_not_verify vs unsupported). It also explains the two distinct verification paths and the coverage of both company-financial and other claims, making it complete for an agent to correctly invoke and interpret results.

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 already covers both parameters with descriptions, giving a baseline of 3. However, the description adds practical guidance: tolerance_pct is described as overriding the claim's implied tolerance and recommended for hallucination detection, and the claim parameter is illustrated with examples. This goes beyond the schema.

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, using specific verbs like 'verify' and 'fact check'. It distinguishes itself from sibling tools by detailing the SEC EDGAR fast path for company-financial claims and the grounded pipeline for all other claims, and by noting 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?

It explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct', providing a clear when-to-use. It also contrasts with the alternative of multiple sequential calls, but does not explicitly list sibling tools to avoid or exclusions for non-claim queries, so it falls slightly short of a 5.

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

ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all perform overlapping routed-search functions; ask_pipeworx_beta is even documented as currently identical to ask_pipeworx. The three Polymarket discovery tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) also have blurry boundaries around finding vs. validating vs. executing on edges.

Naming Consistency4/5

Nearly all tools use snake_case with a verb_noun or descriptive pattern (ask_pipeworx, validate_claim, resolve_entity, list_subscriptions). Minor deviations exist — bare verbs like remember/recall/forget and noun_first names like bet_research or entity_profile — but the convention is largely predictable and readable.

Tool Count2/5

32 tools is heavy, and the set spans unrelated domains: Pipeworx data routing, prediction-market trading, agent memory, subscription management, npm dependency checks, user-agent parsing, and llms.txt generation. The sub-clusters each earn their place individually, but as a single server surface the count is unjustifiably large and scattershot.

Completeness3/5

The Pipeworx research surface is quite complete (lookup, grounded answers, deep research, entity profiles, comparisons, validation, entity resolution, discovery, feedback), and memory/subscription lifecycles are fully covered. However, the server has no coherent single domain — user-agent parsing (the server's namesake) has only one tool, while unrelated utilities like generate_llms_txt and scan_dependency appear with no supporting ecosystem.

Resources