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Glama

Fbi Artcrimes

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?

Annotations already declare read-only/idempotent/non-destructive, and the description adds critical behavioral context: the meaning of 'could_not_verify' (check did not happen, not evidence) and 'unsupported' (no source covered). This prevents a caller from misinterpreting a failure as a refutation, which is exactly the kind of disclosure needed.

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 average but each sentence adds value: usage examples, routing logic, verdict definitions, and a clear caller warning. It is reasonably front-loaded and well-structured, though it could be tightened without losing critical information.

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?

There is no output schema, so the description must carry the full burden of explaining return values and edge cases. It covers verdicts, the grounded/structured value with citation, reasoning, and the error structure for could_not_verify. It also explains the routing logic and the tool's purpose relative to sequential alternatives, making it complete for a composite validation tool.

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 the baseline is 3. The description adds meaningful nuance beyond the schema by explaining that tolerance_pct overrides the claim wording's implied tolerance and recommending 1–2 for hallucination detection. This extra semantic guidance justifies a 4.

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 verifies factual claims against authoritative sources, uses a specific verb (validate/verify), and provides example natural-language usages. It distinguishes itself from sibling research/query tools by positioning as a claim-verification shortcut that replaces a multi-step pipeline.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct' and describes the routing between company-financial and other claims. It does not explicitly name alternative tools or state exclusions, but the context is strong enough to guide selection.

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

A3.7/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers (beta is currently exactly the same as stable), and the Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, bet_research) all scan or price prediction-market opportunities with fuzzy boundaries. Other clusters like memory and subscriptions are clear, but enough overlap remains that an agent can easily misroute a query.

Naming Consistency3/5

Most names are readable snake_case and many follow a verb_noun shape (list_art_crimes, resolve_entity, validate_claim), but the set also contains noun-phrase names (entity_profile, recent_alerts, pipeworx_feedback, deep_research) and brand-prefixed composites (polymarket_kalshi_spread, ask_pipeworx_beta). This is mixed but still scannable; there is no outright convention chaos.

Tool Count2/5

33 tools exceeds the 25+ threshold and is bloated for a server whose name promises FBI art crimes—only two tools relate to that name. Even treated as a Pipeworx platform, the sprawl makes the tool surface harder to navigate than necessary.

Completeness2/5

For the nominal art-crime domain, only list_art_crimes and get_art_crime exist, with no category search, statistics, or art-crime alerting, so an agent expecting art-crime workflows hits dead ends. The unrelated Pipeworx functionality is broadly covered, but that does not make the set complete for its stated server purpose.

Resources