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Glama

Studio Ghibli

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?

This goes well beyond the annotations: it discloses the structured vs. grounded pipeline, the full list of verdict values, return shape (actual value with citation and reasoning), and crucial semantics for could_not_verify and unsupported. It also warns callers not to treat could_not_verify as evidence, which is highly valuable 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 front-loaded with trigger phrases and then structured into usage, routing, return values, and caveats. Every sentence carries substantive information; it is slightly verbose but not padded.

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 there is no output schema, the description fully covers what the caller needs: verdicts, return value with citation and reasoning, failure semantics for both could_not_verify and unsupported, and the routing behavior. It is complete for a tool of this complexity.

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%, but the description adds meaningful guidance beyond the schema: tolerance_pct overrides the claim-implied tolerance, 1–2% is recommended for hallucination detection, and default is implied but capped at 5. This enriches the parameter's semantics.

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 explicit natural-language trigger phrases and states the core function: natural-language claim verification against authoritative sources. It distinguishes itself from sibling tools by describing a specific verdict-based output and by noting it replaces 4–6 sequential calls, making its purpose unambiguous.

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 clearly says 'Use whenever the agent needs to check whether something a user said is factually correct' and delineates two routing paths (SEC EDGAR/XBRL for company financials, grounded pipeline for other facts). It does not explicitly name alternatives or state when not to use it, but the guidance is clear enough for most scenarios.

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

B3.3/5.0
Disambiguation1/5

The set contains multiple near-identical query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and five overlapping prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) that an agent could easily confuse. The Studio Ghibli tools are clear enough, but they are drowned out by a large unrelated cluster with fuzzy boundaries.

Naming Consistency2/5

There are small internally consistent clusters (polymarket_* tools, ask_pipeworx variants, singular/plural Ghibli resource pairs), but the overall set mixes simple nouns (film, person, location), imperative verbs (remember, forget, recall), and descriptive compound names (ai_visibility_check, generate_llms_txt, scan_competitor_ai_presence). The species tool is 'species'/'species_one' while every other resource uses bare singular for the single-item fetch, breaking the otherwise predictable Ghibli pattern.

Tool Count1/5

A server named 'Studio Ghibli' exposes 41 tools, but only 10 of them relate to Ghibli content; the other 31 are an unrelated general-purpose data, prediction-market, memory, and subscription toolkit. This is an extreme mismatch between the apparent purpose and the actual tool surface.

Completeness4/5

For the Ghibli data domain itself, the surface is solid: films, people, locations, vehicles, and species all have list and single-item lookup, plus cross-links between entities. Minor gaps exist, such as no search or filter capability and no way to fetch films by director or year, but the core read-only catalog is well covered.

Resources