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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / tolerance_pct
      Added value: +{
+  "description": "Max 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.",
+  "type": "number"
+}
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Goes far beyond the readOnly/idempotent annotations by explaining the SEC EDGAR fast path vs. grounded pipeline, the full verdict vocabulary, and critical semantics of could_not_verify and unsupported. It also discloses that could_not_verify means the check did not happen and must not be treated as evidence, which is crucial behavioral context.

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 every part serves a purpose: user-phrase examples, use-case trigger, internal routing, return format, and failure-mode caveats. It is front-loaded with the intent phrases and maintains readability, though it is somewhat long and could be trimmed without losing essential information.

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 fully accounts for return values (verdict, value, citation, reasoning), error semantics, and the two execution paths. It even emphasizes the absence of coverage for unsupported claims. Given the tool's complexity, the description covers all necessary context and eliminates ambiguity for the agent.

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, so the baseline is 3. The tool description adds valuable nuance, especially for tolerance_pct, explaining how it overrides implied tolerance and recommending values for hallucination detection, which is more than a restatement of 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 opens with concrete user-phrase examples and explicitly states 'natural-language claim verification against authoritative sources,' giving a specific verb+resource+scope. It clearly distinguishes itself from general querying by focusing on fact-checking claims with structured verdicts, and even notes it 'Replaces 4–6 sequential calls,' setting it apart from siblings.

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?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct,' which gives a clear trigger. It outlines internal routing for financial vs. other claims but does not explicitly name alternative sibling tools or provide when-not-to-use exclusions, stopping 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

Several clusters blur together: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded share routing, while discover_tools/suggest_questions and the Polymarket edge/arbitrage/fill-risk tools have overlapping discovery purposes. Rich descriptions reduce some confusion, but an agent must read carefully to avoid misselection.

Naming Consistency3/5

Most names are lowercase snake_case, but there is no consistent verb_noun pattern: ask_pipeworx/beta/grounded and polymarket_* are domain-prefixed, entity_profile/recent_changes are noun phrases, and query/metadata/remember are bare verbs or nouns. The naming is still readable and subfamilies share prefixes, so it is mixed rather than chaotic.

Tool Count2/5

34 tools is above the comfortable range for a single MCP server, and several could be folded together (the beta variant, visibility checks, and Polymarket scanners). The breadth reflects a large platform, but the surface feels heavy for an agent to select from confidently.

Completeness4/5

The query side is strong: PA Open Data has datasets/metadata/query coverage, and Pipeworx provides ask, deep_research, entity_profile, compare, recent_changes, validate_claim, plus subscriptions and memory with lifecycle coverage. Minor gaps: descriptions promise resolvable pipeworx:// citations but no resource/read tool is exposed, and there is no direct way to fetch an arbitrary record by citation.

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