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Data Mesa

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?

The description discloses critical behavioral semantics beyond the annotations: the distinction between could_not_verify (not evidence, carries verification_error) and unsupported (no source found), which is vital for correct interpretation. It also reveals the two backend paths (SEC EDGAR + XBRL vs. grounded pipeline), adding transparency not captured by readOnly/openWorld hints. No contradiction with annotations.

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 long but front-loaded with examples and a clear purpose statement. Every sentence contributes context—scope, routing, outputs, and caller warnings. It could be slightly trimmed without losing value, but it is well-structured and avoids fluff, earning a 4.

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 compensates by explaining return values (verdict list, actual value with citation, reasoning) and the meaning of each verdict. It covers edge cases (could_not_verify, unsupported), caller warnings, and the tool's advantage over sequential calls, making it a complete guide for invocation and interpretation.

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 coverage is 100%, so the baseline of 3 applies. Both claim and tolerance_pct are already well-described in the schema (including default cap and use cases). The description does not add new parameter-level semantics; it only reiterates the tolerance concept in passing ('exact percent-delta math'), which does not elevate the score above baseline.

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 a specific verb ('validate'/'verify') and resource (natural-language claims), backed by concrete example phrasings. It distinguishes from sibling tools by detailing the two-path pipeline and stating it 'replaces 4–6 sequential calls,' making its unique role explicit.

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 an explicit trigger: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the automatic routing for company-financial vs. other claims. However, it does not explicitly name when-not-to-use or contrast directly with sibling tools like ask_pipeworx, so it stops short of a full 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

A3.6/5.0
Disambiguation3/5

Several tool groups have overlapping purposes (e.g., ask_pipeworx variants, polymarket research tools, visibility checks), despite detailed descriptions. An agent may struggle to choose between closely related options.

Naming Consistency2/5

Tool names mix snake_case verb_noun patterns (e.g., ai_visibility_check, compare_entities) with noun-heavy compound names (e.g., polymarket_arbitrage, deep_research) and single-word names (e.g., query, recall). No consistent convention.

Tool Count2/5

With 34 tools, the surface is heavy for a research/data server. Many tools are variants of core capabilities (ask_pipeworx, polymarket edges), suggesting consolidation would improve usability.

Completeness3/5

The server covers a broad range: data research, prediction markets, entity profiles, monitoring. However, the abundance of specialized variants and gaps in unified workflows (e.g., needing separate tools for simple vs grounded queries) reduce completeness.

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