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Glama

Crc

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?

Annotations already declare read-only, open-world, and idempotent hints, but the description adds substantial behavioral context: the automatic fallback routing, the definition and significance of the `could_not_verify` verdict (with `verification_error` detail), the meaning of `unsupported`, and the fact that it replaces 4–6 sequential calls. None of this contradicts the annotations.

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 every sentence earns its place. It is front-loaded with usage examples, then covers routing, output, and caveats. The structure is logical: examples → general usage → specific behaviors → return values → important caveats → efficiency benefit. No filler or repetition; the length is justified by the tool's complexity.

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?

Given the tool has no output schema, the description must explain return values and behavior, and it does thoroughly. It enumerates the exact verdicts, explains the meaning and proper handling of `could_not_verify` and `unsupported`, and describes the routing logic. The description is sufficient for an agent to select and invoke the tool correctly without additional context.

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 coverage is 100%, but the description goes beyond schema descriptions. For `claim`, it provides concrete examples and clarifies the type of natural-language claim expected. For `tolerance_pct`, it explains the override effect, default behavior (implied by wording, capped at 5), and a specific use case (1–2 for hallucination detection). This adds meaningful semantic depth beyond the schema.

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. It opens with concrete query patterns ("Is it true that…", "fact check") and explicitly distinguishes itself from generic Q&A by returning verdicts. It also differentiates the tool's behavior from siblings by mentioning the structured SEC EDGAR/XBRL fast path for company-financial claims and the grounded pipeline for other 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 when-to-use guidance: "Use whenever the agent needs to check whether something a user said is factually correct." It also explains the routing behavior (company-financial vs. other claims), which helps the agent decide when this tool is appropriate. However, it does not explicitly name sibling tools as alternatives or say when NOT to use it, so it misses the full 'when-not/alternatives' criterion.

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

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in the prediction market domain (e.g., bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) and data querying (ask_pipeworx, ask_pipeworx_grounded, deep_research). This overlap creates confusion for an agent selecting the right tool.

Naming Consistency2/5

Naming is inconsistent: most tools use snake_case but some start with a verb (ask_, bet_, compare_) while others start with a noun (entity_profile, pipeworx_feedback, polymarket_*). There is no uniform pattern, making it harder to predict tool names.

Tool Count2/5

With 32 tools, the server is overstuffed for a single focus. It bundles checksums, AI visibility, data queries, prediction market analysis, subscriptions, memory, and more, which would be better split into separate, more focused servers.

Completeness2/5

Despite the large tool count, the server lacks completeness in key areas: no tool to place prediction market trades, no direct SEC filing detail extraction (only through generic queries), and only two checksum tools despite the server name 'Crc'. The surface feels scattershot rather than comprehensive.

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