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Glama

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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

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive, and the description adds meaningful behavioral context: the distinction between "could_not_verify" (check did not happen, carries verification_error) and "unsupported" (no source covered), and the reversible routing logic. It also explains the output verdict set and the citation format, which is valuable 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 longer than typical but every sentence contributes: examples, use case, routing logic, output definition, error-handling caveat, and efficiency claim. It is front-loaded with easy-to-grep phrases and avoids fluff, though it could be slightly tightened.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema, the description defines the full verdict set and provides critical edge-case semantics. It also explains why this tool is efficient (replaces multiple calls). It does omit some minor details like exact citation format and behavior for "inconclusive", but given the complexity and annotations, it is largely complete.

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 both parameters well-documented. The description does not add new parameter-level meaning beyond the schema; it only reiterates that tolerance overrides wording. Since the schema already handles parameter semantics thoroughly, a baseline 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 opens with concrete example phrasings ("fact check", "verify the claim that…") and immediately identifies the verb+resource: natural-language claim verification against authoritative sources. It also differentiates from siblings by explaining the fast path for company-financial claims versus the grounded pipeline for any other claim, and notes it replaces 4–6 sequential calls.

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 'Use whenever the agent needs to check whether something a user said is factually correct.' It also gives usage nuance for the two claim types (SEC EDGAR vs. grounded pipeline) and instructs callers on how to interpret "could_not_verify" vs "unsupported", which is critical for proper use. It doesn't explicitly name alternatives like deep_research, but the scope is clear.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, aided by detailed descriptions (e.g., ask_pipeworx vs. ask_pipeworx_grounded). However, some overlap exists among Pipeworx-based tools (compare_entities, entity_profile, recent_changes, validate_claim) that could cause confusion without careful reading.

Naming Consistency3/5

Snake_case is used, but naming patterns are mixed: some start with verbs (ask_pipeworx, validate_claim), others with nouns or prefixes (box_get_file, entity_profile). This inconsistency makes it harder to predict tool names.

Tool Count3/5

At 31 tools, the server covers an unusually broad set of domains (cloud storage, data queries, betting, memory, subscriptions). This is on the heavy side and could benefit from splitting into more focused servers.

Completeness3/5

Gaps exist: Box storage lacks create/update/delete, Polymarket tools offer analysis but no execution, and some data domains (e.g., weather) have only indirect coverage. The surface feels uneven across the wide scope.

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