VerifierMCP

verify_math

Check algebra and calculus symbolically using SymPy by providing an equation or an expression with a claimed result to confirm whether it is mathematically valid.

Instructions

Verify algebra/calculus symbolically (SymPy). Pass an equation with '=', or an expression plus claimed_result.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "expression": "<expression>"
    },
    "name": "verify_math"
  }
}

Input Schema

Name	Required	Description	Default
`expression`	Yes
`claimed_result`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 19, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses the engine (SymPy) and that verification is symbolic, but says nothing about failure behavior, error conditions, or limits (e.g., unsupported constructs, timeouts). That is adequate but thin for a tool with zero annotation coverage.

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?

Two sentences, front-loaded with the purpose and immediately followed by the input contract. Every clause carries information, though the parenthetical '(SymPy)' is minor implementation detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be described. The input modes and required field are covered well enough to call the tool, with only minor ambiguity about when claimed_result becomes mandatory.

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 0%, so the description must compensate, and it does: 'expression' is explained as either a full equation containing '=' or a bare expression, and 'claimed_result' as the value to check it against. The only gap is that it doesn't explicitly state claimed_result is required in the bare-expression mode.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (verify) and resource (algebra/calculus expressions) plus the method (symbolic, via SymPy), which cleanly separates it from check_units and test_regex. It never names a sibling or states what it is not, so an agent must infer the boundary against run_code.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The second sentence gives input-shape guidance ('equation with =' vs 'expression plus claimed_result'), which implies two usage modes but not when to prefer this tool over run_code or check_units. There is no explicit when-to-use or when-not-to-use statement.

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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