Skip to main content
Glama

Statuspage

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

A4.4/5.0
Behavior5/5

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

The description goes far beyond the annotations by explaining the verdict types, the meaning of 'could_not_verify' vs 'unsupported', the presence of verification_error with stage/detail, and that 'could_not_verify' must not be shown as evidence. It also details the dual pipeline behavior and citation format, providing rich behavioral transparency.

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 essential information: examples, routing, verdicts, error semantics, and efficiency gains. Every sentence contributes value, though the opening list of example phrasings is somewhat redundant and could be shortened without loss of meaning.

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?

The tool has no output schema, so the description fully compensates by explaining all return values (verdicts, actual value, citation, reasoning) and error conditions. It also covers both financial and non-financial paths, making it complete for an agent to understand the tool's behavior and expectations.

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 covers 100% of parameters with detailed descriptions, especially tolerance_pct which already explains override semantics, range, defaults, and hallucination-detection usage. The description itself adds no new parameter-level detail beyond what the schema already provides, so a baseline score of 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 identifies the tool as natural-language claim verification against authoritative sources, with specific verb+resource and scope. It provides concrete example queries and explains the two processing paths (SEC EDGAR for financial claims, grounded pipeline for others), making its purpose unambiguous.

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?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the internal routing logic and notes that it replaces 4–6 sequential calls, giving clear context on when to invoke it. However, it does not name alternative sibling tools or provide explicit 'when not to use' guidance.

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

All Statuspage 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

A3.8/5.0
Disambiguation2/5

Multiple tools serve the same 'ask a question' function (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) with subtle differences that are hard to distinguish. Entity-oriented tools like entity_profile, recent_changes, and compare_entities also overlap on company information, making selection ambiguous.

Naming Consistency4/5

All tool names are lowercase snake_case and mostly descriptive, with consistent prefixes for tool families (ask_pipeworx, polymarket_*, statuspage_*). However, there is no uniform verb_noun pattern — some are bare verbs (forget, remember, recall) while others are noun phrases (entity_profile, recent_changes) — so predictability is moderate.

Tool Count2/5

With 35 tools, this set is heavily overloaded for a server named Statuspage; only 4 tools actually relate to status pages, while the rest form a broad data query, prediction market, and memory toolkit. The count is far beyond what the name implies and dilutes focus.

Completeness2/5

For a Statuspage server, the tool surface is severely incomplete: it only reads status and incidents and provides no way to create, update, or resolve incidents. Even for the broader data-tool domain, there are gaps — no direct record-fetch by ID and no write/update operations for data sources.

Resources