Skip to main content
Glama

Twilio

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?

Annotations already declare read-only/idempotent/open-world, and the description adds critical behavioral detail: it explains the two processing pipelines, defines all six verdict values, and crucially warns that `could_not_verify` indicates a check failure (with `verification_error`) and must not be treated as evidence. This goes far beyond the annotation hints.

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 most but every section earns its place: query examples, routing logic, verdict semantics, and caller caveats. The 'Replaces 4–6 sequential calls' is a minor extra, but the overall structure is clear and front-loaded with the core purpose.

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?

For a tool with no output schema, the description fully specifies the return shape (verdict, actual value, pipeworx:// citation, reasoning), enumerates all verdict values, and explains ambiguous outcomes like unsupported vs. could_not_verify. It also describes internal routing, making it self-contained 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 description coverage is 100%; both `claim` and `tolerance_pct` have detailed descriptions with examples. The tool description does not introduce additional parameter semantics beyond mentioning 'exact percent-delta math,' so it neither adds nor subtracts value; the baseline of 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 immediately establishes the tool as a natural-language claim verifier with clear examples ('fact check', 'is it true that…'), and explicitly states its function: 'natural-language claim verification against authoritative sources.' It distinguishes itself from siblings by naming the structured SEC EDGAR path for financial claims and the grounded pipeline for all others, plus the specific verdict vocabulary.

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 gives an explicit when-to-use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also clarifies the two routing paths (financial vs. other claims), and notes it replaces 4–6 sequential calls, but 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.

All Twilio 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

B3.2/5.0
Disambiguation2/5

The server is named 'Twilio' but contains only 5 Twilio-specific tools mixed with 31 unrelated Pipeworx tools. An agent must distinguish between Twilio and Pipeworx functionality, and the tool descriptions are clear individually, but the overall set is confusing because the server name implies a focused Twilio service, not a general-purpose data platform with a few Twilio actions.

Naming Consistency2/5

The Twilio tools follow a consistent 'twilio_verb_noun' pattern (e.g., twilio_send_sms, twilio_make_call), while the Pipeworx tools use a separate snake_case convention (e.g., ask_pipeworx, entity_profile, deep_research). The two naming conventions are clearly distinct and do not mix, but the overall set is inconsistent because the server is named after one convention yet the majority of tools follow a different one.

Tool Count1/5

With 36 tools, the count is high, but the critical issue is that only 5 tools are relevant to the Twilio server name. The remaining 31 tools belong to Pipeworx, a completely different domain. This is an extreme mismatch between the claimed server purpose (Twilio) and the actual tool set, making the tool count inappropriate.

Completeness2/5

For a Twilio-focused server, the tool set is very incomplete: it covers only basic SMS sending, call initiation, and listing messages/calls. Missing are phone number management, media handling, conversation features, and other common Twilio operations. The Pipeworx tools are comprehensive for their own domain, but they are irrelevant to the Twilio server's stated purpose, leaving significant gaps.

Resources