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Glama

dadjokes

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?

Even with annotations already indicating read-only and idempotent behavior, the description adds crucial operational context: the meaning of each verdict, especially the distinction between 'could_not_verify' (check did not happen, must not be used as evidence) and 'unsupported' (no source covers it). It also discloses the verification pipeline, error handling, and the guarantee of verbatim evidence with citations.

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 dense but well-structured: it opens with trigger phrases, defines the use case, explains routing, lists verdicts, and adds a warning about a specific error case. It is longer than minimal but every section carries meaningful information. Slightly verbose in the middle but 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?

With no output schema, the description needs to explain return values and edge cases, and it does. It covers the verdict list, the evidence/citation format, the meaning of error states, and the routing logic across claim types. This is complete for a tool of this complexity, even including caller guidance about 'could_not_verify'.

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% coverage for both parameters (claim and tolerance_pct) with clear descriptions. The tool description adds some context about exact percent-delta math and the default tolerance cap, but does not substantially enhance the parameter-level meaning beyond what the schema already states. This matches the baseline of 3 for full schema coverage.

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 a natural-language claim verifier that returns verdicts like confirmed/refuted, with specific verbs ('fact check', 'verify the claim') and a resource ('authoritative sources'). It distinguishes itself from sibling tools by describing a fast path for company-financial claims and a grounded fallback for other factual claims, plus a note that it replaces 4–6 sequential calls.

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 explicitly states when to use the tool: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides routing guidance for financial vs. other claims. However, it does not explicitly name alternative sibling tools or specify when NOT to use this tool in favor of another, so it falls just 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

A4/5.0
Disambiguation3/5

Most tools are clearly distinct, but the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are nearly identical, with the beta explicitly matching the stable version. Additionally, ai_visibility_check and scan_competitor_ai_presence overlap in purpose, creating some selection ambiguity.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (e.g., get_joke, search_jokes, resolve_entity, validate_claim). Even the more noun-like names like entity_profile and pipeworx_feedback fit the readable convention. No mixed camelCase or erratic verb usage.

Tool Count2/5

With 34 tools, the count is far above the typical well-scoped range of 3-15. The server is named 'dadjokes' but only 3 tools actually serve that purpose; the remaining 31 are an unrelated Pipeworx research and prediction-market platform, making the scope feel bloated and misaligned.

Completeness3/5

For the nominal dad-joke domain, the surface is complete (get, random, search). However, the intended domain is ambiguous given the massive unrelated toolset; it's unclear what a user should expect the server to cover, and the analysis/prediction tools lack obvious CRUD or management counterparts.

Resources