Skip to main content
Glama

Digimon

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

A4.5/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the annotations: the two-path internal routing (SEC EDGAR + XBRL fast path vs. grounded pipeline), the exact verdict list, the special meaning of 'could_not_verify' (including the verification_error detail and the warning not to treat it as evidence), and the distinction vs. 'unsupported'. It also explains the tolerance_pct override behavior and the citation format. This is rich, non-redundant context.

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 opens with concrete trigger phrases, explains the dual pipeline, lists the exact verdict enums, clarifies edge cases, and mentions the efficiency gain. It is front-loaded with the core purpose and structured logically. No filler or repetition.

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?

For a tool with no output schema, the description adequately covers the return values (verdict list, actual value with citation, reasoning) and special error semantics. It also explains what happens for different claim types, the routing logic, and the meaning of 'could_not_verify' vs. 'unsupported'. This is complete for the tool's complexity and the available metadata.

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?

Schema coverage for parameters is 100%, so the description doesn't need to re-explain them. The description does add a little context (e.g., 'exact percent-delta math' and the mention of tolerance_pct in the context of hallucination detection), but the input schema already provides robust descriptions for both parameters, including defaults and cap. Thus a baseline 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 states what the tool does: natural-language claim verification against authoritative sources. It gives concrete example phrasings ('Is it true that…"), specifies the two routing paths (SEC EDGAR for company-financial claims, grounded pipeline for all other claims), and names the return verdicts. This distinguishes it from generic lookup or search 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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also clarifies the internal routing (company-financial claims vs. other claims) and notes that it replaces 4–6 sequential calls, implying when a single-call unified verification is preferred. However, it doesn't explicitly name alternative tools or exclusion criteria, so it stops short of a full when/ when-not comparison.

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

All Digimon 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

A3.9/5.0
Disambiguation2/5

Multiple tools overlap significantly: the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are near-identical routers, and the six polymarket_* tools plus bet_research cover overlapping prediction-market territory. The server name 'Digimon' adds confusion since almost all tools are unrelated to Digimon, making it hard for an agent to tell what this server is actually for.

Naming Consistency3/5

All names use snake_case, which is consistent, but the pattern is mixed: some tools start with verbs (get_digimon, search_digimon, list_subscriptions, validate_claim), others with nouns (entity_profile, polymarket_arbitrage, pipeworx_trending), and a few are bare verbs (remember, recall, forget). This irregularity makes the naming less predictable than a uniformly verb-first set.

Tool Count2/5

With 33 tools, the server exceeds the 'too many' threshold and feels bloated. The tools span Digimon data, Pipeworx research, Polymarket betting, memory, subscriptions, and even llms.txt generation—an incoherent grab-bag that doesn't form a focused, well-scoped toolkit.

Completeness4/5

For the broad data-research and prediction-market domain implied by the majority of the tools, the surface is impressively comprehensive: universal routing, grounded answers, deep research, entity resolution, company/drug profiles, comparisons, claim validation, memory, subscriptions, discovery, and multiple specialized Polymarket tools. Minor gaps exist (e.g., no direct order execution on Polymarket), but ask_pipeworx routes to thousands of sources, covering most needs.

Resources