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Doffin

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. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The description significantly extends beyond the readOnly/idempotent annotations by explaining the dual-pipeline behavior, the verdict taxonomy, and especially the crucial semantics of 'could_not_verify' and 'unsupported' — warning callers that 'could_not_verify' is not evidence and must not be presented as one. This is a rich, honest disclosure of potentially confusing behaviors.

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 well structured and front-loaded with examples, then explains the pipeline and return values, and closes with a clearly labeled 'IMPORTANT for callers' warning. It is reasonably concise given the complexity; though a bit long, every sentence adds value.

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 having no output schema, the description fully covers return values (verdict types, actual value with pipeworx:// citation, reasoning) and error semantics (could_not_verify vs unsupported). It explains both processing paths and even notes that it replaces 4-6 sequential calls, making the tool's behavior completely understandable for an agent.

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 (claim and tolerance_pct), with the schema already providing detailed descriptions including default behavior and ranges. The tool description does not add parameter-specific semantics beyond the schema, so the baseline of 3 applies.

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's purpose: natural-language claim verification against authoritative sources. It provides multiple example phrasings ('Is it true that...', 'fact check', 'verify the claim that...') and distinguishes this tool from siblings by explaining it replaces 4-6 sequential calls and covers both financial and non-financial 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?

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 clarifies the scope by describing how company-financial claims go through a fast path while any other factual claim falls through to the grounded pipeline. However, it does not name alternative tools or provide explicit when-not-to-use guidance, which would push it to 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

A3.9/5.0
Disambiguation2/5

Several tool clusters have fuzzy boundaries: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and ask_pipeworx/deep_research/validate_claim overlap for factual research. Polymarket_arbitrage, polymarket_edges, and bet_research also all surface betting opportunities, while ai_visibility_check and scan_competitor_ai_presence serve nearly the same purpose.

Naming Consistency3/5

Names are readable and mostly snake_case, but the pattern is mixed: doffin_* and polymarket_* use domain-prefixed nouns, ask_pipeworx* uses verb+product, and remember/recall/forget/subscribe are bare verbs. There is no single predictable verb_noun convention, though each internal cluster is somewhat consistent.

Tool Count2/5

34 tools is heavy for a server named Doffin, and only three tools actually relate to Norwegian procurement. The remaining surface is mostly general Pipeworx data access, prediction-market analysis, and memory utilities, which feels like several servers bundled under one misleading name.

Completeness3/5

For Doffin read-only access, search + recent + notice detail covers the core workflow well. However, the server's actual domain is fragmented across procurement, Pipeworx research, prediction markets, memory, and subscriptions, which makes coverage difficult to reason about and leaves minor gaps such as no subscription update capability.

Resources