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Intact

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

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds critical behavioral details: explains the meaning of could_not_verify (check did not happen, carries verification_error, must not be shown as evidence), distinguishes it from unsupported, and clarifies the two routing paths. This is essential context that annotations alone do not provide.

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 highly structured and front-loaded with trigger phrases. Every sentence adds value: usage context, routing logic, return values, and caller warnings. No fluff or repetition; the length is justified by the tool's 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?

With no output schema, the description adequately describes return values (verdict types, actual value with citation, reasoning) and error semantics. It also covers the two processing paths and the replacements for sequential calls, making it self-contained for an agent to select and use the tool correctly.

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%, so baseline is 3. The description adds extra semantics for tolerance_pct, explaining how it overrides the implied tolerance and advising 1–2 for hallucination detection. This provides guidance beyond the schema, making the parameter more actionable.

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 a fact-checking/claim verification tool with specific trigger phrases. It distinguishes between company-financial claims (SEC EDGAR/XBRL fast path) and other claims (grounded pipeline), which differentiates it from siblings like deep_research or ask_pipeworx_grounded.

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 gives explicit usage guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also notes that it replaces 4–6 sequential calls, implying a consolidated alternative. However, it does not explicitly state when not to use it or name alternatives for exclusion.

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

A4/5.0
Disambiguation3/5

Most tools have distinct purposes, but ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research form a confusing cluster—especially since ask_pipeworx_beta is currently identical to ask_pipeworx. The Polymarket tools are highly specialized and mostly separable, and interaction_count/find_interactions have clear but overlapping scopes.

Naming Consistency4/5

The dominant pattern is verb_noun snake_case (ask_pipeworx, compare_entities, resolve_entity, validate_claim), which is predictable and readable. There are some noun-style names like entity_profile, recent_changes, and interaction_count, plus brand-prefixed families like pipeworx_* and polymarket_*, but the conventions are consistent enough within families.

Tool Count2/5

33 tools is beyond the 25+ threshold and the set spans several unrelated domains—molecular interactions, npm dependency scanning, llms.txt generation, AI brand visibility, and prediction-market arbitrage—making it feel like multiple servers merged into one. Several niche tools could be consolidated or split into separate MCP servers, and ask_pipeworx_beta adds redundancy.

Completeness4/5

Core workflows are very well covered: lookup/grounded answering/deep research, tool discovery, entity resolution, profiles and comparisons, claim validation, memory CRUD, subscription lifecycle, and prediction-market analysis from edge detection to fill-risk. Minor gaps include no direct fetch tool for pipeworx:// citation URIs and some soft-failing data sources, but agents can work around those.

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