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Glama

icme-preflight-mcp

by io.icme.api

check_logic

Catch contradictions in reasoning before acting on it. FREE — no account needed. Extracts quantitative and logical claims from any plan, calculation, or chain of thought, then uses a Z3 SAT solver to mathematically prove whether they contradict each other. This is formal verification, not an LLM second-guessing itself. Returns CONSISTENT, CONTRADICTION, or UNKNOWN with the extracted claims.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
show_smtNoInclude the generated SMT-LIB2 formula in the response for inspection (default: false)
reasoningYesThe reasoning, plan, or chain of thought to check. Be specific — include numbers, conditions, and constraints for the best results (max 2000 chars)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • changedInput schema / properties / reasoning / description
      Previous value: -"Agent reasoning, plan, or chain of thought to check for contradictions (max 2000 chars)"New value: +"The reasoning, plan, or chain of thought to check. Be specific — include numbers, conditions, and constraints for the best results (max 2000 chars)"
    • changedInput schema / properties / show_smt / description
      Previous value: -"If true, include the generated SMT-LIB2 formula in the response (default: false)"New value: +"Include the generated SMT-LIB2 formula in the response for inspection (default: false)"
  2. First observed

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries full responsibility and excels: it states the tool is free, requires no account, uses a Z3 SAT solver for mathematical proof, and returns specific outcomes (CONSISTENT, CONTRADICTION, UNKNOWN) with extracted claims. It also clarifies that it is not an LLM re-check, providing important behavioral 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 three sentences, each earning its place: a clear action-oriented first sentence, a method explanation, and an output clarification. It is front-loaded and free of filler, making it easy to scan.

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?

The tool is simple (one required string param) and the description fully covers purpose, method, outputs, and prerequisites. It explains the return values, which is particularly helpful since no output schema exists. The 2000-char limit is in the schema, so it need not be repeated.

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%, so the baseline is 3. The description adds contextual examples ('include numbers, conditions, and constraints') but this largely echoes the schema's own guidance. It does not deepen the semantics of show_smt beyond its schema description, so no extra credit is warranted.

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 the tool's function: 'Catch contradictions in reasoning before acting on it.' It specifies the resource (reasoning/plan/calculation) and the method (Z3 SAT solver), and distinguishes it from sibling tools like check_action or check_relevance by focusing on logical consistency rather than action verification or relevance.

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 provides clear usage context: 'before acting on it' and 'any plan, calculation, or chain of thought.' It also clarifies that this is formal verification, not LLM self-review, which helps agents decide when to use it. However, it does not explicitly name alternatives or exclusion cases, so it stops short of a full 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

A4.2/5.0
Disambiguation4/5

Each tool has a distinct role, but the presence of payment-method variants (check_action vs check_action_paid, create_account vs create_account_card, top_up vs top_up_card) introduces minor overlap that is clearly explained. The check tools are differentiated by output detail and purpose, though quick_check and check_action could be confused at a glance.

Naming Consistency4/5

Names predominantly follow a verb_noun pattern (check_action, create_account, run_tests), with consistent _paid and _card suffixes for payment variants. Minor deviations like quick_check (adjective_noun) and top_up (phrasal verb) are understandable but break the pattern slightly.

Tool Count5/5

With 14 tools, the set covers the full lifecycle of policy creation, testing, action checking, payment, and proof verification without bloat. Each tool serves a clear purpose within the service's scope.

Completeness4/5

The primary workflow (create policy, test policy, check actions, verify proofs) is well covered, including multiple payment options. Missing utilities like listing or deleting policies and credit balance checks are minor gaps that don't block the core use case.

Resources