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Glama

ukpolice

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, but the description adds essential non-obvious behavior: could_not_verify means the check failed and carries verification_error (NOT evidence), unsupported means no source covered, and the exact verdict list is disclosed. This goes well beyond the annotations and gives callers clear expectations for edge cases.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with trigger phrases and a clear purpose, then moves efficiently through routing, return values, and caveats. Every sentence carries meaningful operational detail without fluff, making it long but appropriately sized for the tool's complexity.

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?

With no output schema, the description fully covers what the tool returns: verdicts, actual value with pipeworx:// citation, reasoning, and structured error info. It also explains the two execution paths and tolerances, making it self-sufficient for an agent to decide and interpret calls correctly.

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%, and the schema already fully describes both 'claim' (with examples) and 'tolerance_pct' (with overrides and default). The description does not add parameter-level semantics beyond what the schema provides, so 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 defines a specific verb+resource: 'natural-language claim verification against authoritative sources' with concrete trigger phrases like 'fact check' and 'verify the claim that…'. It clearly distinguishes itself from sibling tools by outlining judgment verdicts (confirmed, refuted, etc.) and a structured pipeline, making it the go-to for factual claim checking rather than general queries.

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?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and describes two routing paths (SEC EDGAR for company financials, grounded pipeline for others). It mentions replacing 4–6 sequential calls as an alternative, but does not explicitly name sibling tools or exclusions, so it stops short of a full when-not/alternatives breakdown.

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

A3.6/5.0
Disambiguation2/5

Several clusters of tools overlap heavily: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all answer questions, and the five polymarket_* tools require careful reading to distinguish. The three UK police tools are clear, but they sit among many near-duplicate data-query and memory utilities.

Naming Consistency2/5

Mixed conventions: snake_case verb_noun (get_crimes) coexists with brand-style names (ask_pipeworx), noun phrases (polymarket_arbitrage), and bare verbs (forget, recall). The polymarket_* and ask_pipeworx_* families are internally consistent, but the overall set lacks a single pattern.

Tool Count2/5

34 tools is heavy, and only three relate to the server's stated ukpolice domain, while the rest form a general-purpose data platform. Many meta-tools (suggest_questions, discover_tools, pipeworx_feedback, pipeworx_trending, memory) add bulk relative to the core purpose.

Completeness3/5

For the actual Pipeworx scope, coverage is strong: query, research, comparisons, subscriptions, memory, and feedback are all present. But for the ukpolice name, it is missing many UK police endpoints (neighborhoods, stop-and-search, etc.) and has no write/update operations, so the surface feels mismatched.

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