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Glama

Ooni

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. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses the two underlying execution paths, that evidence is verbatim, and crucially explains the nuanced semantics of could_not_verify vs unsupported, including the verification_error field and 'must not be shown as evidence.' This is rich behavioral context well beyond the 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 well-structured: trigger phrases, use-case, routing, return values, and error semantics each earn their place. The opening trigger phrase list is somewhat redundant, but the overall structure is clear and information-dense without being 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 explains the return values (verdicts, actual value, citation, reasoning). It covers the two code paths, the critical error distinction, and the efficiency benefit (replaces 4–6 call). This is complete for a tool of this complexity.

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?

The schema covers both parameters (claim and tolerance_pct) with clear descriptions, so the baseline is 3. The description adds little parameter-specific meaning—it mentions 'exact percent-delta math' but does not directly elaborate on tolerance_pct or claim format beyond what the schema provides.

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 identifies a specific verb and resource: natural-language claim verification against authoritative sources. It provides trigger phrases ('fact check', 'verify the claim that...') and distinguishes itself from generic Q&A by detailing two verification pipelines (SEC EDGAR fast path and grounded pipeline), effectively differentiating from sibling tools.

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 states when to use the tool ('Use whenever the agent needs to check whether something a user said is factually correct') and provides routing guidance for company-financial vs. other claims. However, it does not mention when not to use it or name alternatives (e.g., ask_pipeworx_grounded for open-ended queries), so it misses explicit exclusions.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but the ask_pipeworx family (including beta and grounded) and deep_research overlap in functionality, causing some ambiguity. The OONI tools are well-differentiated, and other tools are distinct.

Naming Consistency4/5

Naming predominantly follows a verb_noun pattern with underscores, but some tools like generate_llms_txt and pipeworx_feedback deviate slightly. Overall consistent with minor inconsistencies.

Tool Count4/5

35 tools is on the higher side but justified by the broad domain coverage (data lookup, censorship monitoring, prediction markets, memory, etc.). Each tool serves a specific purpose, making the count reasonable.

Completeness4/5

The tool surface covers core workflows comprehensively, including data retrieval, censorship analysis, prediction market evaluation, and memory management. Minor gaps exist (e.g., no subscription modification tool), but overall it's well-scoped.

Resources