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Fara

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

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses critical behavioral nuances: the distinction between could_not_verify and unsupported, the meaning of verification_error, the special handling of company-financial claims with exact percent-delta math, and the routing to live sources with verbatim evidence. This is valuable context an agent needs to interpret results correctly, especially that could_not_verify is not evidence for or against the claim.

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 every sentence earns its place: trigger phrases, routing logic, return values, caller warnings, and efficiency rationale. It is structured with a clear progression from examples to internal behavior to output semantics. No redundant fluff despite the complexity.

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 and absence of an output schema, the description fully compensates by explaining the verdict enum, citation format, reasoning, and the specific meaning of failure statuses. It also provides operational context (when to use, why it replaces sequential calls) and aligns with sibling differentiation. Complete enough for an agent to select and invoke it correctly.

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

Parameters3/5

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

Schema description coverage is 100%; both parameters (claim and tolerance_pct) are already fully documented with examples, ranges, and defaults. The tool description mentions 'exact percent-delta math' but adds no essential meaning beyond the schema. Baseline 3 is appropriate.

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 defines the tool as natural-language claim verification against authoritative sources, with a specific verb and resource. It distinguishes itself from sibling tools by detailing the two distinct internal paths (SEC EDGAR/XBRL fast path and grounded pipeline) and explicitly stating it replaces 4–6 sequential calls, making its composite purpose unambiguous.

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?

Provides clear context: "Use whenever the agent needs to check whether something a user said is factually correct." It also implies the alternative of sequential manual calls ("Replaces 4–6 sequential calls") but does not explicitly state when not to use it or name a specific sibling alternative. Good but not perfect 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

A3.9/5.0
Disambiguation3/5

There are several clusters of tools with overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all do data-fetching/research with somewhat subtle differences. Polymarket tools also overlap (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, bet_research, polymarket_kalshi_spread). However, most tools have detailed descriptions that clarify their distinct roles, and the core data-lookup tools are differentiated by grounding level and scope.

Naming Consistency4/5

Most tools follow a consistent verb_noun or noun_verb pattern (e.g., search_registrants, list_foreign_principals, get_registrant_documents, subscribe, unsubscribe, remember, recall, forget, resolve_entity, validate_claim). Deviations include brand-name tools like ask_pipeworx, pipeworx_feedback, pipeworx_trending, and polymarket_kalshi_spread that mix conventions but are still readable and predictable within their domain.

Tool Count2/5

34 tools is heavy for a single MCP server, especially with multiple overlapping research entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions). The broad data-router nature of the server explains the size, but it is still a large surface that would be better consolidated.

Completeness4/5

The server covers its visible domains well: data lookup, grounded verification, entity profiling, comparison, change tracking, subscription lifecycle, memory, and FARA-specific queries. Minor gaps exist (e.g., no direct tool for updating saved memory beyond forgetting/re-remembering, no tool to create custom alert types beyond the three supported categories), but the core workflows are complete.

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