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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

A4.4/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description discloses two distinct execution paths (structured SEC EDGAR/XBRL vs grounded pipeline), the exact meaning of could_not_verify (check did not happen, carries verification_error) vs unsupported (no source found), and the verdict enum. It also flags that could_not_verify must not be shown as evidence, which is critical 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 front-loaded with the purpose and trigger examples. It is long, but every sentence contributes necessary caveats (could_not_verify vs unsupported, routing logic, efficiency benefit). The structure is a single dense paragraph, which could be improved with separation, 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 fully specifies the verdict values, citation format, error semantics, and both processing paths. It also preempts common caller mistakes (e.g., misinterpreting could_not_verify). For a tool with this complexity, the description is remarkably complete.

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% and the schema already includes rich descriptions for both claim and tolerance_pct, including the hallucination-detection override mention. The tool description restates these points and adds a brief example, but does not meaningfully expand beyond the schema's parameter documentation.

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 trigger phrases and states 'natural-language claim verification against authoritative sources'. It clearly distinguishes the tool from siblings by specifying company-financial claims get a structured SEC EDGAR fast path while all other claims route to a grounded pipeline, and notes it replaces 4–6 sequential calls.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct.' This gives a clear when-to-use. It does not name specific alternative tools or provide explicit when-not-to-use scenarios, but implies that non-fact-checking queries would use other tools.

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
Disambiguation3/5

Many tools have closely related or overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), and ask_pipeworx_grounded all route to the same underlying toolset, while discover_tools and suggest_questions both help agents discover capabilities. The polymarket_* family also has several opportunity-scanning tools with subtle differences, though detailed descriptions help clarify.

Naming Consistency3/5

Names are mostly snake_case but follow mixed patterns: verb-first (ask_pipeworx, validate_claim), noun-first (entity_profile, bet_research, la_recent), and bare verbs (remember, forget, unsubscribe). The prefix groups (la_, pipeworx_, polymarket_) show some consistency, but there is no uniform verb_noun convention.

Tool Count2/5

34 tools is well into the 'too many' range for a single server. The surface bundles several distinct domains—structured data querying, prediction markets, LA open data, memory, subscriptions, and npm scanning—making it feel like a kitchen sink rather than a focused toolset.

Completeness4/5

Within each bundled sub-domain, coverage is strong: query/grounded/research/entity-profile/compare/validate covers data workflows; polymarket tools include research, edge scan, arbitrage, fill-risk, and cross-venue spread; LA data has search/query/recent; memory and subscription lifecycles are fully CRUD. Minor gaps exist (e.g., no way to browse LA dataset attributes beyond search), but no major dead ends.

Resources