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Glama

jikan

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

A4.7/5.0
Behavior5/5

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

The description goes beyond annotations by explaining verdict semantics, especially the crucial distinction between could_not_verify (check didn't happen) and unsupported (no source exists). It also discloses error payload structure and the presence of verbatim evidence citations, which annotations do not cover.

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 dense but every sentence earns its place. It opens with concrete query examples, then proceeds logically through routing, return values, and special cases, with no redundancy or filler.

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 list, actual values, citations, reasoning), error handling, and behavioral edge cases. It also explains the internal routing and the rationale for using the tool, making it complete for a complex tool.

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% with detailed parameter descriptions, so the baseline is 3. The tool description does not add new parameter-level semantics beyond what the schema already provides (e.g., tolerance_pct override behavior is fully in the schema), though it does connect tolerance_pct to the 'exact percent-delta math' mentioned in the description.

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 with specific query patterns like 'is it true that' and 'fact check'. It distinguishes itself from siblings by detailing two distinct verification paths (SEC EDGAR for company-financial, grounded pipeline for other claims) and the specific verdicts it returns.

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?

It explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct,' and gives concrete routing guidance for company-financial vs. other claims. It also notes this tool replaces 4–6 sequential calls, implying it should be used instead of chaining multiple operations.

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

A3.6/5.0
Disambiguation2/5

Many tools serve overlapping purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research, bet_research, compare_entities, entity_profile, recent_changes, validate_claim) all querying Pipeworx data with similar outcomes. An agent would struggle to choose correctly without deep understanding of nuanced differences.

Naming Consistency2/5

Naming conventions are mixed: snake_case (ask_pipeworx, get_anime), camelCase (generate_llms_txt, pipeworx_feedback), and compound names (polymarket_arbitrage, ai_visibility_check). No consistent pattern across the tool set.

Tool Count2/5

34 tools is excessive for a single server. Many are meta-tools (discover_tools, suggest_questions) or narrowly focused (pipeworx_trending, scan_dependency). The server tries to cover too many domains (anime, financial data, predictions, memory) in one surface.

Completeness3/5

The anime tools (search, get, top) form a reasonable read-only surface. The Pipeworx query tools are comprehensive but lack obvious data management tools (e.g., listing sources, managing credentials). Memory tools (remember/recall/forget) are isolated. Overall, gaps exist but core workflows are covered.

Resources