Skip to main content
Glama

Wmata

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

A4.5/5.0
Behavior5/5

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

Goes well beyond the readOnly/idempotent annotations by explaining the decision logic for financial vs non-financial claims, enumerating the six possible verdicts, and warning that 'could_not_verify' means the check did not happen and must not be presented as evidence. This error semantics is critical behavioral disclosure not derivable from annotations.

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 efficient and every sentence adds value, but it's a single dense paragraph with no section breaks. The core purpose and usage are front-loaded, and caveats come later. Length is justified by the tool's complexity, but it is slightly verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema and moderate complexity, the description fully covers routing, verdicts, error semantics, and efficiency gains. It explains what is returned (verdict + value + citation + reasoning) without needing a formal output schema. The only gap is the lack of a structured example, but overall it's complete.

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?

The input schema already describes both parameters at 100% coverage, so the description doesn't need to add parameter-level detail. It does add a hint about 'exact percent-delta math' and tolerance overriding behavior, but that's already in the schema. Baseline 3 applies.

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 natural-language trigger phrases, then states it performs 'natural-language claim verification against authoritative sources.' It clearly distinguishes this from sibling research/analysis tools by scoping it to fact-checking a user's claim and noting it replaces 4-6 sequential calls. Specific verb+resource (verify claims) makes purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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' — a clear trigger condition. It also differentiates two routing paths (SEC EDGAR fast path vs grounded pipeline) for financial vs non-financial claims, helping the agent decide wiring. No exclusions are stated, but the trigger is so clear that alternatives aren't needed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Wmata toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation3/5

Most tools have distinct purposes with detailed descriptions, but the server mixes WMATA transit tools (bus routes, rail predictions) with unrelated general-purpose tools (deep_research, polymarket_arbitrage). This broad scope can confuse an agent expecting a focused transit server.

Naming Consistency2/5

All names use snake_case, but there is no consistent verb_noun pattern. Many are noun_noun (bus_routes, rail_lines) or verb_properNoun (ask_pipeworx), and the naming conventions vary wildly across different domains (e.g., validate_claim vs entity_profile vs scan_dependency).

Tool Count2/5

41 tools is excessive for a WMATA transit server. The vast majority of tools (ask_pipeworx, deep_research, polymarket_*) are not related to WMATA, making the tool surface bloated and unfocused for its stated purpose.

Completeness3/5

The WMATA-specific tools cover core bus and rail operations (incidents, predictions, routes, stations). However, gaps like fare info and elevator status are missing. The non-transit tools are extensive but irrelevant to the server's apparent focus.

Resources