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Glama

swisstransport

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

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses critical behavioral details: the dual-pipeline architecture, the precise meaning of each verdict (especially distinguishing could_not_verify as a non-result), the requirement not to treat could_not_verify as evidence, and the unsupported meaning. This is exactly the kind of context that helps an agent handle edge cases correctly.

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 every sentence earns its place: trigger phrases, pipeline details, verdict semantics, and failure-mode warnings. It could be more front-loaded (the purpose statement appears after the examples), but it remains efficient and well-structured for a tool with this 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 thoroughly covers the return values: verdict list, actual value with citation, and reasoning. It also addresses edge cases (could_not_verify vs unsupported) and explains the tool's efficiency (replacing 4–6 calls). For a claim-verification tool, this is complete and actionable.

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 both parameters already have rich descriptions (claim with examples, tolerance_pct with range and default). The description itself adds little beyond what the schema provides; it mentions 'exact percent-delta math' but the schema already explains the tolerance behavior. 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 clearly states the tool performs natural-language claim verification against authoritative sources, with trigger phrases like 'fact check' and 'verify the claim that...'. It distinguishes itself from sibling Q&A tools by focusing on verdicts and providing two distinct verification pipelines (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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct,' providing a clear when-to-use. However, it does not explicitly mention when not to use it or name alternative tools (e.g., ask_pipeworx_grounded for general queries), so it stops short of full when-not/alternatives guidance.

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

A3.9/5.0
Disambiguation3/5

Most tools have distinct names and purposes, but the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) creates ambiguity as they serve overlapping needs with slight variations. Additionally, the transport tools (get_connections, get_stationboard, search_stations) are clearly distinct from the rest, but the overall set mixes domains, making it harder for an agent to know which tool to pick.

Naming Consistency3/5

All tool names use snake_case, but there is no consistent pattern: some start with verbs (get_, list_, search_, remember, forget), some with nouns (entity_profile, recent_changes, recent_alerts), and others with adjectives (ai_visibility_check, deep_research). This inconsistency, while not chaotic, makes the set less predictable.

Tool Count1/5

The server name 'swisstransport' implies a narrow Swiss transport focus, but with 34 tools, only 3 are transport-related. The count is vastly inappropriate for the suggested purpose. Even considering the actual broad domain (data query, prediction markets, memory), 34 tools is on the high side and likely overwhelming for any single server.

Completeness4/5

Inferring the domain from the tool descriptions, the set covers a wide range of capabilities: data query (ask_pipeworx, deep_research), entity comparison (compare_entities), prediction markets (polymarket_*), memory (remember/recall), subscriptions, and more. There are few obvious gaps given the scope; for example, broader financial data is accessible through ask_pipeworx. The transport subset is minimal but present.

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