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Glama

exchange

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

A4.5/5.0
Behavior5/5

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

Even with readOnlyHint/openWorldHint/idempotentHint annotations covering the safety profile, the description adds substantial behavioral context: the crucial distinction between could_not_verify (check did not happen, not evidence) and unsupported (no source exists), the mention of verbatim evidence and citations, and the claim that it replaces 4–6 sequential calls. This goes far beyond annotations and materially helps the agent interpret results.

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?

Although longer than average, the description is well-structured and every sentence earns its place. It front-loads trigger phrases and core purpose, then flows into routing logic, return values, and critical caller notes. There is no redundancy or filler—dense but efficient.

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 lacking an output schema, the description comprehensively covers both processing paths, all six verdicts, citation format, and the error semantics of could_not_verify/unsupported. An agent has enough context to call the tool correctly and interpret its response confidently.

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 provides 100% parameter coverage with detailed descriptions and examples for both claim and tolerance_pct. The description adds only minor usage context (e.g., 'set 1–2 for hallucination detection'), which does not significantly enhance what the schema provides. 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 is highly specific: it states the tool verifies natural-language factual claims against authoritative sources, lists trigger phrases, and describes two distinct routing paths (SEC EDGAR + XBRL for company-financial claims, grounded pipeline for others). It enumerates the exact verdict types returned, making its purpose unmistakable and differentiating it from generic search or research tools.

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 explicit when-to-use guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the internal routing logic. However, it does not name alternative sibling tools or provide explicit when-not-to-use scenarios, so it falls short of a 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

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions. Potential confusion exists between similar tools like ask_pipeworx and ask_pipeworx_grounded, but the descriptions differentiate them well. The high number of tools across multiple domains could still cause some ambiguity, but overall an agent can distinguish them.

Naming Consistency3/5

Naming patterns are mixed. Some subgroups follow consistent patterns (polymarket_*, pipeworx_*, get_*), but overall the set includes verb_noun (list_subscriptions, get_rate, remember) and noun_verb (entity_profile, deep_research) without a unified convention. The mixture of imperative and descriptive names reduces predictability.

Tool Count2/5

34 tools is excessive for a single server, bundling unrelated domains (currency, memory, data query, prediction markets, dependency scanning). This scope could be split into multiple servers for better coherence. The high count may overwhelm agents and increases the risk of misselection.

Completeness3/5

Each sub-domain (e.g., currency, memory, Polymarket) has reasonable coverage with common operations present. However, the server name 'exchange' is misleading—it suggests a narrower focus. Some gaps exist (e.g., no batch currency conversion, no direct Polymarket market detail tool). Overall, the set covers many tasks but lacks a unified purpose.

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