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Slack_connect

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

A4.5/5.0
Behavior5/5

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

Despite annotations already covering safety (read-only, idempotent), the description adds crucial behavioral semantics: the meaning of could_not_verify (check did not happen, must not be shown as evidence) versus unsupported, the verdict categories, and the distinction between the SEC EDGAR fast path and the grounded pipeline. This goes far beyond what annotations provide.

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 dense yet well-structured: opens with natural-language trigger examples, defines the core purpose, explains the two routing paths, lists the return values, and provides an important caller warning. Every sentence adds value without redundancy.

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 the return verdicts, the grounded or structured actual value with citation, and the reasoning field. It also clarifies error semantics (could_not_verify vs unsupported) and mentions the tolerance behavior, 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.

Parameters3/5

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

The input schema already documents both parameters thoroughly (claim with examples, tolerance_pct with range and override behavior), so schema coverage is 100%. The description does not add new parameter-level meaning beyond what the schema states, so the 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 it performs natural-language claim verification against authoritative sources, with a specific verb and resource. It distinguishes two paths (company-financial via SEC EDGAR, and any other factual claim via grounded pipeline), making its purpose unmistakable and distinct from sibling tools like deep_research or ask_pipeworx.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct,' providing a clear when-to-use. However, it does not explicitly name alternatives for non-verification queries or state when not to use, though the routing details and replacement of multiple calls give good context.

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

A3.5/5.0
Disambiguation2/5

The five Slack tools are distinct, but the rest of the set is a sprawling bundle of Pipeworx, prediction-market, memory, and subscription tools with several overlapping pairs: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, while discover_tools and suggest_questions both act as discovery entry points and scan_competitor_ai_presence wraps ai_visibility_check. An agent would frequently have to read long caveats to choose the right tool.

Naming Consistency3/5

Most names are descriptive snake_case and the Slack tools share a clean slack_ prefix, but the broader set mixes verb-led names (ask_pipeworx, generate_llms_txt, validate_claim) with noun-style names (entity_profile, pipeworx_trending, ai_visibility_check) and a few bare verbs (remember, recall, forget, subscribe). It is readable but does not follow a single predictable pattern.

Tool Count2/5

36 tools is above the 25+ threshold and far more than a Slack connector needs: only five tools actually interact with Slack, while the other 31 are unrelated Pipeworx research, prediction-market, memory, and subscription features. The set reads as a kitchen-sink bundle rather than a focused integration.

Completeness2/5

For a Slack_connect server, the surface is only partially complete: it can list channels/users, join, read history, and send messages, but common Slack operations like threads, reactions, message update/delete, channel creation/archiving, and direct messages are missing. The unrelated data tools do not fill these gaps, so the actual Slack domain would still cause agent failures.

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