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Glama

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

A4.4/5.0
Behavior5/5

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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds critical behavioral details beyond these: the distinction between the structured and grounded pipelines, the exact verdict list, the meaning of 'could_not_verify' (check did not happen, not evidence) and 'unsupported', and the caveat about verification_error. This is rich context that an agent needs to interpret results correctly.

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 and front-loaded with trigger phrases and use cases. Each sentence adds value: two pipeline paths, return values, and important caveats. It is not overly terse, but the length is justified by the tool's complexity and the need to communicate failure semantics.

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?

The tool has no output schema, so the description carries the full burden of explaining return values. It does this thoroughly: verdicts, actual value with pipeworx:// citation, reasoning, and explicit handling of 'could_not_verify' and 'unsupported'. It also explains the two distinct execution routes, making the description complete for a tool of this complexity with minimal parameters.

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 input schema has 100% coverage for both parameters, including detailed descriptions of 'claim' with examples and 'tolerance_pct' with its override semantics and default behavior. The description itself does not add parameter information beyond what the schema already provides, so a baseline score of 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 explicitly states the tool's purpose: natural-language claim verification against authoritative sources, with trigger phrases like 'fact check' and 'is it true that…'. It also distinguishes itself from sibling tools by noting it replaces 4–6 sequential calls and returns a verdict, making it unique among the listed 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 clearly states when to use: 'whenever the agent needs to check whether something a user said is factually correct.' It also describes the two execution paths (structured SEC/EDGAR for financial claims, grounded pipeline for everything else), providing clear context. However, it does not explicitly name alternative tools or exclusions, so it stops short of exhaustive guidance.

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

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (entity_profile, compare_entities, ask_pipeworx, etc.). However, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are very similar, and suggest_questions/discover_tools overlap slightly. Overall, tools are well-disambiguated with only minor ambiguity.

Naming Consistency3/5

All tool names use snake_case, but the pattern is inconsistent: some start with a verb (ask_, validate_), others with a noun (entity_, bet_). Several are noun_noun (entity_profile), and some are single verbs (remember). No consistent verb_noun pattern across the set.

Tool Count2/5

33 tools is excessive for a server named 'Isbn' that only has 2 ISBN-specific tools. The majority are from Pipeworx, creating scope mismatch. Multiple redundant tools (three ask_pipeworx variants, several polymarket tools) inflate the count unnecessarily.

Completeness2/5

For a server named 'Isbn', completeness is very low—only conversion and validation are provided; no metadata lookup or other ISBN-related features exist. The Pipeworx tools themselves are comprehensive, but they do not align with the server's implied domain.