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Glama

Mapbox

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
      {
  "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

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the bar is lower, yet the description adds substantial behavior: two verification pipelines, the verdict taxonomy, citation format, and the critical caller warning that could_not_verify is not evidence and carries verification_error{stage,detail}. This goes well beyond what structured fields provide.

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 densely packed with high-value trigger phrases, routing logic, verdict semantics, and a caller warning. It is front-loaded with purpose and every sentence contributes meaning, though it is slightly verbose and could be tightened without losing value.

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 having no output schema, the description compensates by explaining return verdict types, actual value with citation, reasoning, and error semantics. It covers the two major branching scenarios and clarifies ambiguous outcomes, making the tool nearly self-sufficient for an agent to invoke 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?

Input schema has 100% coverage and already fully documents both parameters, including tolerance_pct ranges, default, and usage guidance. The tool description does not add extra parameter semantics beyond what the schema already provides, so a 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 opens with concrete natural-language trigger phrases and states the core action: verifying factual claims against authoritative sources. It clearly distinguishes itself from sibling tools by covering both a structured SEC EDGAR fast path and a general grounded pipeline, so an agent knows exactly what this tool is for.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct,' providing a strong usage signal. It also explains routing between company-financial claims and other claims, but does not name alternative sibling tools or state explicit when-not-to-use scenarios, so it is clear though not exhaustive.

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

Many tools have overlapping purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) and several tools serve similar data-retrieval functions, making it difficult for an agent to distinguish which to use.

Naming Consistency4/5

Tool names mostly follow a consistent verb_noun pattern (e.g., geocode_forward, generate_llms_txt, resolve_entity). A few less descriptive names (forget, recall) exist but overall naming is predictable.

Tool Count2/5

38 tools is far too many for a server branded as 'Mapbox'. Only about 8 tools directly relate to map/geospatial functionality; the rest are unrelated (Pipeworx data, Polymarket, memory). The scope is dramatically overextended.

Completeness2/5

The Mapbox-specific tools lack coverage of major features like style management, tilesets, or data upload. The non-Mapbox tools cover their domains moderately, but the server's overall completeness for its named purpose (Mapbox) is severely lacking.

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