batch
Run multiple simulation requests in one call and return per-item success or failure details.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | Simulation requests forwarded to POST /v1/batch. |
Run multiple simulation requests in one call and return per-item success or failure details.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | Simulation requests forwarded to POST /v1/batch. |
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 of behavioral disclosure. It only mentions per-item success/failure details, omitting important aspects like ordering, atomicity, rate limits, or error handling for the batch as a whole.
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?
A single sentence of 16 words efficiently conveys the core purpose. No unnecessary details, and it is front-loaded with the main action.
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 simplicity (1 required param) and no output schema, the description adequately states return details. However, it lacks information about maximum batch size, required fields in each item, and whether results are ordered.
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 100%, and the description adds minimal value beyond what the schema provides (e.g., mentioning forwarding to a specific endpoint). It does not elaborate on the structure or constraints of the items array.
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 runs multiple simulation requests in one call, distinguishing it from the sibling 'simulate' which likely handles single requests. The verb 'run' and resource 'simulation requests' are specific.
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 implies batch processing for multiple simulation requests, but it does not explicitly state when to use this over alternatives like 'simulate' or provide scenarios where batch is preferable. No exclusions or prerequisites are mentioned.
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.