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Cataas

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

A4.8/5.0
Behavior5/5

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

The description discloses critical behavioral nuances beyond annotations: it explains the meaning of each verdict, especially 'could_not_verify' as a failure state that must not be used as evidence, and defines 'unsupported'. It also reveals the dual-path execution (structured SEC EDGAR vs grounded pipeline) and the citation format. This goes well beyond the readOnly/openWorld/idempotent hints.

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 each section serves a purpose: trigger examples, usage directive, routing explanation, return values, and critical caller warnings. It is structured from general purpose to specific caveats. Slightly dense, but the complexity justifies the length; no fluff.

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 covers return values (verdict types, actual value, citation, reasoning), including edge cases like 'could_not_verify' and 'unsupported'. It also explains the underlying data sources and the performance benefit. Given the tool's complexity and the lack of an output schema, this is a complete and self-sufficient description.

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% with rich descriptions for both parameters. The tool description adds contextual meaning by mentioning 'exact percent-delta math' and the tolerance override for hallucination detection, which complements the schema's tolerance_pct description. It doesn't repeat the schema but reinforces the intent, so a slight upgrade from baseline is warranted.

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 the tool's function: natural-language claim verification against authoritative sources. It uses specific verbs like 'verify', 'fact check', 'confirm or refute', and gives concrete trigger phrases. It distinguishes itself from siblings by describing its unique verdict-based output and automatic routing between structured financial data and grounded pipeline.

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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It further differentiates handling of company-financial claims vs any other factual claim, providing clear guidance on when the tool applies. It does not explicitly name alternatives but describes its role as replacing 4–6 sequential calls, which is strong usage direction.

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

A3.7/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route natural-language questions to the same underlying data catalog, and the polymarket_* family has several near-synonymous scanning tools. Entity_profile, compare_entities, and recent_changes also have fuzzy boundaries. Only the three cat tools are clearly distinct, but an agent could easily pick the wrong tool across the Pipeworx family.

Naming Consistency3/5

The names are consistently snake_case and several families share clear prefixes like ask_pipeworx, polymarket_, and list_. However, the set mixes imperative verb-first names with noun-style names, and the cat_* tools do not share any naming pattern with the dominant Pipeworx family.

Tool Count2/5

34 tools is too many for a server named Cataas, and only 3 of them are actually cat-related. The other 31 tools belong to an unrelated data-research and prediction-market platform, making the tool count inappropriate for the apparent purpose.

Completeness2/5

As a cat-image server, the surface is thin: random_cat, cat_by_tag, and list_tags cover basics but omit common Cataas operations like GIFs or text-on-cat. As a data-research platform, the cat tools are noise, so the mixed set is incomplete and incoherent for either apparent purpose.

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