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Glama

Excuse

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

A4.6/5.0
Behavior5/5

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

Annotations declare read-only, open-world, idempotent, and non-destructive hints. The description adds crucial behavioral nuances beyond this: the distinction between could_not_verify (check failed, not evidence) and unsupported (no source found), the structured vs grounded pipeline behavior, and the warning that could_not_verify must not be shown as evidence. This is rich, actionable context.

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 structured and purposeful. It starts with trigger phrases, then explains usage, pipeline routing, return values, and error semantics in a logical order. Every section adds necessary detail for a complex tool, though it is slightly verbose in places (e.g., the 'IMPORTANT for callers' note could be tightened).

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 and absence of an output schema, the description thoroughly covers what is needed: the two execution paths, exact return verdict types, the actual value and citation format, and the meaning of error-related outcomes. It effectively equips an agent to decide when to invoke it and what to expect.

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?

Schema coverage is 100% for both parameters, so baseline is 3. The description adds value for tolerance_pct by explaining override semantics, hallucination detection use, and default behavior (implied by wording, capped at 5). This moves it above baseline, though claim's semantic is adequately covered by examples in the schema.

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 identifies the tool's purpose: verifying natural-language factual claims against authoritative sources. It uses strong, specific language ('fact check', 'verify the claim') and distinguishes itself from siblings by stating it replaces 4–6 sequential calls and by detailing two distinct verification paths (SEC EDGAR for financial claims, grounded pipeline for others).

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 when to use the tool ('Use whenever the agent needs to check whether something a user said is factually correct') and differentiates between company-financial and other factual claims. However, it does not explicitly name alternative tools or provide 'when not to use' guidance, so it misses the full bar for 5.

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
Disambiguation2/5

The ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) all route to the same underlying 5,708-tool catalog with subtle behavioral differences that are easy to misselect. Entity-focused tools (entity_profile, compare_entities, recent_changes, scan_competitor_ai_presence) also overlap heavily in the data they return, making boundaries fuzzy.

Naming Consistency3/5

All names use snake_case, but the word-order convention is mixed: verb-first (ask_pipeworx, compare_entities, discover_tools) coexists with noun-first (entity_profile, polymarket_arbitrage, recent_changes). The inconsistent reversal in excuse_generate versus generate_llms_txt further breaks the predictable pattern.

Tool Count2/5

At 32 tools the set is well past the well-scoped range, and several entries feel like padding: ask_pipeworx_beta duplicates ask_pipeworx, while the seven Polymarket tools and five company-intelligence tools could each be consolidated into fewer distinct capabilities. The broad scope justifies a large surface, but this count makes the server difficult to navigate.

Completeness3/5

The core data-lookup lifecycle (resolve, fetch, ground, validate, research, compare) is well covered, and memory/subscription features round it out. Obvious gaps include no direct tool to read a pipeworx:// citation URI (despite deep_research referencing one), and the excuse_generate tool is an isolated one-off with no supporting tools in its domain.

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