Skip to main content
Glama

Dwds

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

A4.4/5.0
Behavior5/5

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

Beyond annotations, the description discloses critical behavior: could_not_verify means the check did not happen and carries verification_error{stage,detail}, and unsupported means no source was found. It warns against treating could_not_verify as evidence, which is essential for correct agent use. The readOnlyHint, openWorldHint, and idempotentHint annotations are fully consistent with the description.

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 long but well-structured: it opens with natural-language examples, states the use case, explains the two execution paths, summarizes the return value, and provides important caveats. While it could be slightly trimmed, each sentence contributes necessary context for a complex tool.

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?

Since there is no output schema, the description fully explains the return envelope: verdict types, actual value with pipeworx:// citation, and reasoning. It also addresses failure modes (could_not_verify vs unsupported), which is crucial for interpreting results. The description leaves no major knowledge gaps for an agent deciding whether and how to invoke the tool.

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 is 100% for both parameters, and the description adds no additional meaning beyond what the schema already provides. The examples for 'claim' and the tolerance guidance for 'tolerance_pct' are embedded in the schema descriptions, so the tool description does not compensate further.

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 natural-language claim verification against authoritative sources, with concrete query examples and a verdict taxonomy. It distinguishes itself from sibling tools by focusing on fact-checking with a structured vs grounded pipeline, rather than general research or search.

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 it: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between company-financial claims and other factual claims, and explains the two execution paths. It does not name alternative tools for other kinds of requests, but the context is clear enough.

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

All Dwds 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

A3.7/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical; the Polymarket betting tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) also overlap. The server name suggests a German dictionary, but most tools are unrelated, causing confusion about the set's focus.

Naming Consistency2/5

Tool names lack a consistent pattern: some use verb_noun (ask_pipeworx, compare_entities), some noun_noun (ai_visibility_check, dwds_frequency), some single words (forget, lemma), and some are acronyms (kwic). This mixed convention makes prediction of tool names difficult.

Tool Count3/5

At 36 tools, the set is large but not extreme. However, it attempts to cover too many domains (German language, prediction markets, AI visibility, npm packages, etc.), making it feel bloated and unfocused for a server named 'Dwds'.

Completeness2/5

For the German dictionary focus implied by the server name, tools are minimal (dwfs_frequency, lemma, snippet) and two are retired. For the broader data platform, there are gaps like no dedicated SEC filing search tool, relying on generic ask_pipeworx. The set feels incomplete for both intended purposes.

Resources