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Dst Dk

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

A4.4/5.0
Behavior5/5

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

Even with annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds substantial behavioral context: the dual routing paths (SEC EDGAR vs. grounded pipeline), the complete verdict list, the crucial caveat that could_not_verify is not evidence and carries verification_error, and the meaning of unsupported. This goes far beyond the annotations and helps callers interpret results correctly.

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 longer than the minimal ideal, but every sentence packs information: examples, routing logic, return values, and a critical caller-facing warning. It is well-structured with a clear progression from user intent to implementation to output semantics. It could be tightened slightly, but it is not wasteful.

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 carries the burden of explaining return behavior. It does so thoroughly: it lists the six verdicts, describes the value with citation, mentions reasoning, and clarifies error semantics for could_not_verify vs unsupported. It also covers the two input modes (implicit tolerance vs. explicit override). This is complete for a tool of this complexity.

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% for both parameters, so the baseline is 3. The description adds some useful context about tolerance_pct (e.g., 'set 1–2 for hallucination detection' and 'default implied by wording, capped at 5'), but this is a behavioral nuance rather than a fundamental semantic addition. The schema already explains what the parameters do.

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 uses the specific verb-phrase 'natural-language claim verification against authoritative sources' and gives multiple natural-language triggers ("fact check", "verify the claim that…"). It also details the structured SEC EDGAR path for company-financial claims and a fallback grounded pipeline for other claims, making its scope and differentiation from sibling tools clear.

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 when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains that it replaces 4–6 sequential calls, which implies a preference over manual multi-step pipelines. However, it lacks explicit 'when not to use' or alternative tool names, so it misses the top score.

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

A3.6/5.0
Disambiguation2/5

Several tools have overlapping functionality, especially in the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the polymarket group (bet_research, polymarket_edges, etc.). Descriptions acknowledge these overlaps, making it difficult for an agent to choose the correct tool without deep understanding.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use snake_case (ask_pipeworx, bet_research), others use camelCase or PascalCase (ai_visibility_check, generate_llms_txt, polymarketArbitrage?). Also, tools like 'list_subjects' and 'get_data' have no clear pattern with the rest.

Tool Count2/5

With 35 tools, the server feels overloaded and tries to cover too many distinct domains (data queries, prediction markets, subscriptions, memory, national statistics). This broad scope reduces coherence and makes the tool set harder to navigate.

Completeness3/5

The tool set covers a wide range of capabilities, from data retrieval to prediction market analysis and subscription management. However, the inclusion of Denmark-specific statistics tools (e.g., list_subjects, get_data) seems out of place and creates a niche gap for users interested in other national datasets.

Resources