Compare multiple named actions/options and get a ranked recommendation. Use when you need to choose between two or more alternatives with uncertainty.
| Name | Required | Description | Default |
|---|---|---|---|
| actions | Yes | List of options to compare (minimum 2) | |
| n_simulations | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It reveals that the tool handles uncertainty and produces a ranking, implying simulation or probabilistic modeling. However, it does not disclose whether the tool is read-only, what side effects exist, or the exact algorithm used (e.g., Monte Carlo simulation indicated by n_simulations parameter).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, consisting of two short sentences that are front-loaded with the core purpose, followed by a usage guideline. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the tool (simulation-based recommendation), the description lacks important details such as what the output looks like, how to interpret the recommendation, and the role of n_simulations. There is no output schema to compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50%, and the description adds some value by aligning with the 'actions' parameter, but it does not explain the 'objective' or 'variables' fields within actions, nor the 'n_simulations' parameter. The description merely repeats the schema's indication that multiple options are compared.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool compares multiple actions/options and produces a ranked recommendation, using the specific verb 'compare' and resource 'actions/options'. It distinguishes from siblings like 'compare' by adding the context of uncertainty. The phrase 'get a ranked recommendation' clarifies the output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use when you need to choose between two or more alternatives with uncertainty', providing a clear usage context. However, it does not mention when not to use this tool or suggest alternatives for cases without uncertainty.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct resources or actions, but there is some overlap (e.g., run_repository_fix vs run_repository_pipeline vs simulate_repository) that could cause confusion. Overall, descriptions help differentiate.
Tool names are primarily snake_case with a verb_noun pattern, but there are inconsistencies (e.g., single-word verbs like 'simulate', 'tokenize', and mixed prefixes like 'preview_', 'product_'). The pattern is readable but not uniform.
With 140 tools, the server is extremely over-scoped for typical MCP usage. This overwhelms agents and suggests poor separation of concerns, likely violating the principle of minimal tool surfaces.
The tool set covers a wide range of functionalities including data onboarding, simulation, decisions, repository management, and admin operations. Minor gaps exist (e.g., no update_agent_run), but core workflows are well-supported.
