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Glama

Amtrak

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. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description discloses the routing behavior, verdict set, error semantics (verification_error for could_not_verify), and the distinction between unsupported and could_not_verify. This is rich, non-contradictory 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 dense but well-structured: lead examples, explicit purpose, routing explanation, return details, and a highlighted IMPORTANT note. It is longer than average but every sentence adds value, so it remains effective.

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?

Despite no output schema, the description fully explains return values (verdict, actual value with citation, reasoning) and the special semantics of could_not_verify and unsupported. For a tool with this complexity, it covers all key aspects needed by an agent.

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 already fully describes both parameters, but the description adds meaningful usage context for tolerance_pct (override implied wording, set 1–2 for hallucination detection, default capped at 5) and provides clarifying examples for claim. This goes beyond the schema.

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 immediately identifies the tool with query examples and states 'natural-language claim verification against authoritative sources', using a specific verb+resource. It distinguishes itself from siblings by describing the two-path routing (SEC EDGAR vs grounded pipeline) and noting it replaces 4–6 sequential calls.

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 gives an explicit trigger: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also clarifies the special handling of financial vs non-financial claims and warns against misinterpreting could_not_verify. However, it does not explicitly name alternative tools or state when not to use it.

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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all answer questions; polymarket_edges and polymarket_arbitrage both find opportunities; and memory tools (remember, recall, forget) are separate but simple. This makes it hard for an agent to pick the right tool without careful reading.

Naming Consistency2/5

Naming is inconsistent: some tools use verb_noun (generate_llms_txt, discover_tools), others noun_noun (entity_profile, amtrak_station_info), and some are single verbs (remember, forget). The Amtrak tools follow a pattern but the rest do not, leading to a chaotic mix.

Tool Count2/5

With 35 tools but only 4 actually related to Amtrak, the server is severely over-scoped for its stated purpose. The majority of tools are unrelated, making it feel bloated and misnamed.

Completeness1/5

For an Amtrak server, the tool surface is severely incomplete: lacks booking, schedules, ticket info, delay details beyond worst delay, and station amenities. The tiny Amtrak subset is a mere fraction of what users would expect.

Resources