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Glama

Quiver

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

A5/5.0
Behavior5/5

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

Annotations already mark readOnly/openWorld/idempotent, but the description adds crucial behavioral context: 'could_not_verify means the check did not happen... is NOT evidence' and includes verification_error, while 'unsupported means we looked and cover no source.' This goes well beyond annotations and prevents misuse.

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 efficiently organized: trigger phrases, usage context, routing behavior, return format, and caller warnings. Every sentence adds unique value and has a clear logical flow, with no redundant or filler content.

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 documents return values (verdict enum, actual value with citation, reasoning) and distinguishes failure semantics (could_not_verify vs unsupported). It also covers both the structured financial path and the general grounded pipeline, making the tool's behavior entirely predictable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds extra meaning beyond the schema: tolerance_pct is explained as overriding implied tolerance and 'set 1–2 for hallucination detection,' and the claim parameter is illustrated with concrete examples. This enhances the agent's ability to set parameters correctly.

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 ('Is it true that…', 'fact check') and clearly states the tool performs 'natural-language claim verification against authoritative sources.' This distinct purpose sets it apart from sibling research/analysis tools, and the explicit verdict list confirms its unique role.

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?

States unequivocally 'Use whenever the agent needs to check whether something a user said is factually correct,' and explains routing logic for both company-financial and other claims. Explicitly mentions it 'Replaces 4–6 sequential calls,' giving an alternative/comparison that helps an agent decide.

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

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose with detailed descriptions. Overlapping areas like polymarket tools are well-differentiated by function (research, arbitrage, edge tracking, fill risk, cross-venue spread). The meta-tools (discover_tools, suggest_questions) further reduce ambiguity.

Naming Consistency5/5

All tool names use a consistent snake_case pattern (e.g., ask_pipeworx, bet_research, polymarket_edges). The naming is descriptive and predictable, aiding agent selection.

Tool Count3/5

34 tools is on the high side for typical MCP servers. While many are justified by the platform's breadth, the count slightly exceeds the ideal range, potentially overwhelming agents.

Completeness4/5

The tool set covers a wide array of domains (company data, drugs, prediction markets, economics, news, etc.) with CRUD-like operations for subscriptions and memory. Minor gaps exist (e.g., no direct social media or custom API tool), but overall the surface is well-matched to the server's data platform purpose.

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