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Glama

Prayer Times

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

A4.4/5.0
Behavior5/5

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

Beyond the annotations (read-only, idempotent), the description discloses nuanced behavior: can return could_not_verify meaning the check did not happen (carrying verification_error), unsupported meaning no source covers it, and warns not to treat could_not_verify as evidence. This is critical behavioral context not available in labels.

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 relatively long but packed with necessary details, structured logically: examples → purpose → routing → return values → caveats. It front-loads user intents immediately, and while some repetition exists (e.g., explaining both paths twice), each sentence adds value.

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 lack of an output schema, the description fully explains return values (verdicts, actual value, citation, reasoning) and explains special verdict meanings. It also covers failure modes and scope (what sources are consulted), making the tool's behavior predictable in a wide range of contexts.

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%, so the schema already documents both parameters well. The description adds examples for the claim format and mentions tolerance behavior (e.g., "set 1–2 for hallucination detection"), but these are supplemental rather than essential, matching the baseline of 3.

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 states the tool's function: natural-language claim verification against authoritative sources, with examples like "Is it true that…" and "fact check." It distinguishes itself from siblings by explicitly addressing fact-checking, and differentiates financial vs. non-financial claim paths, making its purpose unmistakable.

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 says "Use whenever the agent needs to check whether something a user said is factually correct," providing a clear trigger. While it does not list when-not-to-use or alternative tools, the instruction is strong enough to guide selection, and it also mentions efficiency benefits by replacing 4–6 sequential calls.

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, though there is some overlap between `ai_visibility_check` and `scan_competitor_ai_presence`, and between `bet_research` and `polymarket_edges`. Overall, an agent can distinguish them.

Naming Consistency3/5

Tool names follow snake_case but vary in prefix usage (e.g., `pipeworx_*`, `polymarket_*`, no prefix) and verb presence (e.g., `discover_tools` vs. `generate_llms_txt`). The pattern is readable but inconsistent.

Tool Count2/5

With 24 tools, the server is overloaded for the 'Prayer Times' name. Many tools are unrelated to prayer, suggesting poor scoping relative to the server's implied purpose.

Completeness2/5

For prayer times, the server includes core tools but lacks features like multi-day forecasts or advanced settings. However, the inclusion of many unrelated tools makes the set incoherent and incomplete for any single domain.

Resources