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Glama

Duration

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

A4.8/5.0
Behavior5/5

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

The description goes well beyond the read-only/idempotent annotations by explaining the SEC EDGAR fast path, the grounded pipeline fallback, return verdict types, and the critical distinction between could_not_verify (pipeline failure, not evidence) and unsupported (no source). This is rich behavioral context that helps callers interpret results safely.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but information-dense, with a front-loaded trigger phrase list, clear behavioral segmentation, and a dedicated caller warning section. Every sentence earns its place, and the structure flows logically from purpose to usage to return values to caveats.

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?

With no output schema, the description fully explains what the tool returns: verdict types, actual value with citation, and reasoning. It also covers failure modes and error handling (verification_error with stage/detail), making it contextually complete for a claim-verification tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already covers both parameters at 100%, but the description adds significant meaning: tolerance_pct has its range (0.5–50), default behavior ('implied by wording, capped at 5'), and a concrete use case (set 1–2 for hallucination detection). The claim parameter also gets realistic examples, making both parameters more actionable.

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 states the tool's purpose: 'natural-language claim verification against authoritative sources' with explicit trigger phrases like 'fact check' and examples. It distinguishes itself from sibling tools by noting it replaces 4–6 sequential calls and narrowing scope to factual claims.

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?

Provides explicit use context: 'Use whenever the agent needs to check whether something a user said is factually correct' and differentiates sub-paths for company-financial vs other claims. However, it lacks explicit alternatives or 'when not to use' guidance, so it earns a strong 4 rather than 5.

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

A4.1/5.0
Disambiguation3/5

The tool set includes several overlapping tools: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve very similar purposes with only subtle differences in behavior (beta/grounded). Additionally, deep_research and validate_claim partially overlap with these. The prediction market tools are numerous but distinct, and memory/duration tools are clear. Overall, an agent would face some confusion when selecting among the ask_pipeworx variants.

Naming Consistency4/5

Most tools use consistent underscore_case with descriptive verb_noun patterns (e.g., list_subscriptions, resolve_entity, validate_claim). A few memory and subscription tools are single-word verbs (forget, recall, remember, subscribe, unsubscribe), which deviates slightly but remains readable. Overall naming is predictable and clear.

Tool Count3/5

With 33 tools, the server is on the heavier side. The server covers a broad scope (data queries, prediction markets, AI visibility, memory, utilities, subscriptions), which justifies many tools, but some feel redundant (e.g., three variants of ask_pipeworx, multiple polymarket edge tools). A more focused set (around 20-25) would be more typical for coherence.

Completeness4/5

The tool surface is extensive, covering factual Q&A via a universal router, entity profiles, comparisons, prediction market analysis, memory, subscriptions, AI visibility, and utilities. The meta-tool ask_pipeworx provides access to thousands of structured sources, so most data needs are addressable. Minor gaps include no direct tool for specific SEC filing retrieval beyond the meta-router, but this is covered indirectly.

Resources