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Glama

Skiddle

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 readOnly/openWorld/idempotent annotations, the description discloses important behavior: the dual-path execution, the verdict vocabulary, the presence of citations, the critical distinction between 'could_not_verify' (check did not happen) and 'unsupported' (no source found), and a warning not to treat 'could_not_verify' as evidence. This is rich, non-obvious context.

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 front-loaded with query examples and a one-sentence definition, followed by concise, information-dense elaboration. Every sentence earns its place, including caveats and efficiency notes, without being bloated.

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?

For a tool with no output schema, the description fully covers return values (verdict types, actual value, citation, reasoning), error semantics, and usage boundaries. It leaves no critical operational ambiguity for an agent.

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 both parameters are already well-documented. The description adds no extra parameter-specific meaning (e.g., tolerance_pct override behavior is only in schema), so baseline 3 is appropriate.

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') with a clear resource ('natural-language claim verification against authoritative sources') and includes user-intent examples. It distinguishes itself from sibling tools by explicitly covering both structured and grounded pipelines, making its scope unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an explicit 'Use whenever...' directive for factual verification, clarifies that it handles both company-financial and any other factual claims via automatic fall-through, and notes that it replaces 4–6 sequential calls. This gives clear context and efficiency rationale, though it does not name alternative tools explicitly.

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

Most tools have clearly distinct roles, but the ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research cluster overlaps heavily as question-answering and research entry points, with ask_pipeworx_beta currently being an exact duplicate of ask_pipeworx. The prediction-market tools are individually differentiated but numerous enough that selecting the right one requires careful reading.

Naming Consistency4/5

The set is predominantly snake_case and mostly readable, with sensible prefixes like ask_, search_, polymarket_, and scan_. However, conventions mix verb-first names (remember, subscribe, validate_claim), noun-style names (categories, event, entity_profile), and adjective-noun names (recent_alerts, recent_changes), so the pattern is not fully uniform.

Tool Count2/5

34 tools is well above the 25-tool threshold where a server starts to feel heavy, and many could be consolidated (7+ prediction-market tools, 4+ overlapping ask/research tools, plus memory and subscription helpers). The breadth is somewhat justified by the Pipeworx data platform, but the surface is still overloaded for a single server.

Completeness4/5

For the broad data/research domain, coverage is strong: lookup, grounded verification, deep research, entity profiles, comparisons, change feeds, tool discovery, memory, and subscription lifecycle all have coherent coverage. Minor gaps exist, such as no explicit tool to read a pipeworx:// citation URI directly and a fairly thin Skiddle events side beyond search/detail/categories.

Resources