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Clickup

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.

TDQS

A4.6/5.0
Behavior5/5

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

Even with strong annotations (readOnly, idempotent, openWorld), the description adds essential behavioral nuance: the critical distinction between 'could_not_verify' (verification did not happen, not evidence) and 'unsupported' (no source covers it). It also discloses the fallback pipeline and tolerance override semantics, going well beyond annotation hints.

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 organized, front-loading trigger phrases and clearly separating the fast path, fallback, return values, and caller warnings. A few clauses are run-on, but every sentence adds substantive value, so it earns a strong score.

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 fully explains return values (verdicts, actual value, citation, reasoning) and the error/edge-case semantics of could_not_verify and unsupported. It also covers routing and the efficiency benefit, making it complete for a tool of this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful parameter context: tolerance_pct overrides the wording-implied tolerance, is capped at 5, and can be set to 1–2 for hallucination detection. This guidance is not present in the schema alone.

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 explicit trigger phrases and a specific verb-resource pairing. It distinguishes itself from sibling research tools by focusing on confirming/refuting factual claims and returning verdict categories.

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?

It explicitly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also separates company-financial claims from other claims and describes the routing. It does not name alternative sibling tools, but the context and fallback behavior give clear usage guidance.

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
Disambiguation3/5

Many tools have distinct purposes, but there is overlap among query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and among prediction market tools. Detailed descriptions help differentiate, but the large number of tools increases chance of misselection.

Naming Consistency2/5

Naming is highly inconsistent, mixing snake_case and camelCase conventions. ClickUp tools use 'clickup_' prefix while Pipeworx tools have varied patterns (ask_, scan_, validate_, etc.). No unified convention across the set.

Tool Count2/5

35 tools is excessive for a server named 'Clickup', with only 6 ClickUp-specific tools. The remaining 29 are from Pipeworx, which is unrelated. The count is inappropriate for the server's stated purpose.

Completeness2/5

The ClickUp integration is incomplete, missing basic CRUD operations like update and delete tasks. The Pipeworx side is comprehensive but not relevant to the server's name. For a ClickUp server, coverage is poor.

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