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Glama

Singstat Sg

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

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses the two-pipeline routing (SEC EDGAR for financial claims vs. grounded pipeline for others), the exact verdict options, and the critical distinction between 'could_not_verify' and 'unsupported'. It also explains the verification_error payload and how to interpret non-results, which is essential behavioral context not captured by 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 longer than the ideal two-sentence example but front-loads the purpose with concrete examples and use cases. Each subsequent section (pipeline routing, verdict types, error semantics, efficiency claim) earns its place with actionable details. It is well-structured and avoids repetition, though it could slightly trim redundant phrasing.

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?

Given the tool's complexity (two pipelines, nuanced verdicts, error states) and the absence of an output schema, the description thoroughly covers return values, verdict semantics, and error handling. It also states the efficiency advantage over sequential calls, making it self-contained for an agent to invoke and interpret results.

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?

The schema already provides descriptions for both parameters (100% coverage). The description adds significant nuance: tolerance_pct overrides the wording-implied tolerance with a default cap of 5, and is explicitly useful for hallucination detection where material errors must be refuted. It also explains claim routing based on claim content, enriching the semantic understanding of the 'claim' parameter.

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 it performs natural-language claim verification against authoritative sources, using specific verbs like 'fact check' and 'verify the claim'. It distinguishes from sibling tools by focusing on factual claim checking and noting it replaces 4–6 sequential calls, making its purpose unambiguous.

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 provides clear usage context: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between company-financial claims and other claims, and describes when to use a custom tolerance for hallucination detection. However, it does not explicitly name alternative sibling tools or state when not to use it beyond the general claim focus.

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
Disambiguation5/5

Each tool has a clearly defined purpose with detailed descriptions, and despite some composite tools, there is no ambiguity in when to use which one.

Naming Consistency3/5

Tool names mix verb_noun and noun_noun patterns, with some purely verb names, lacking a consistent convention. While readable, the pattern is not predictable.

Tool Count3/5

With 23 tools, the server is on the heavy side for a mixed-purpose toolset. Each tool earns its place, but the count feels slightly bloated for the breadth of domains covered.

Completeness3/5

The server covers several domains (AI visibility, betting, entity lookup, etc.) with reasonable depth, but the lack of a unified domain means some areas feel under-served (e.g., no update/delete except memory).

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