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Permid

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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, so the bar is lower. The description adds high-value behavioral context: distinct verdicts, 'could_not_verify' means the check did not happen and must not be treated as evidence, 'unsupported' means no source coverage, and verification_error fields. This goes well beyond annotations and clarifies important failure semantics. 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 packed with necessary detail: usage, routing, return values, and caller caveats are all present. It is front-loaded with example phrasings and flows logically, though the density could overwhelm some agents. Each sentence adds value; minor redundancy in the pipeline description prevents a 5.

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 there is no output schema, the description fully compensates by enumerating the verdict values, the actual value with citation, reasoning, and the verification_error payload. It also clarifies the distinction between 'could_not_verify' and 'unsupported' — critical for correct caller behavior. The tool is complex, and this description covers all essential aspects, so a 5 is warranted.

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% and both params already have descriptions. The description adds extra meaning beyond the schema: it explains 'tolerance_pct' overrides the claim wording's implied tolerance, gives a concrete range (0.5–50), and suggests 'set 1–2 for hallucination detection'. This is useful guidance not present in the schema, justifying a 4.

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 states a specific verb+resource: verifying natural-language claims against authoritative sources. It distinguishes from siblings by detailing the SEC EDGAR fast path and grounded fallback, and even notes it replaces a multi-step orchestration. Example phrasings ('fact check', 'verify the claim that…') make the tool's purpose unmistakable.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct', providing a clear context. It also implies an alternative (replaces 4–6 sequential calls) and explains routing for company-financial vs. other claims, but lacks explicit when-not guidance or named sibling alternatives, 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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the set includes three very similar `ask_pipeworx` variants that could cause confusion despite detailed descriptions. The Polymarket and memory tools are well-differentiated.

Naming Consistency3/5

Names follow a mix of patterns: some `verb_noun` (ask_pipeworx, compare_entities), some `noun_noun` (entity_profile, polymarket_arbitrage), and some standalone nouns (forget, recall). While readable, the inconsistency is notable.

Tool Count3/5

At 33 tools, the server is on the heavy side for typical MCP servers (3-15 ideal). However, the broad scope covering many data domains partially justifies the count.

Completeness4/5

The tool set covers a wide range of data access and analysis needs, including entity resolution, company profiles, betting analysis, subscriptions, and memory. Minor gaps exist (e.g., no direct tool for editing entities), but overall it's well-rounded.

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