Get a project's resource overview (counts grouped by resource type and status).
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var. | |
| project_id | Yes | The project ID to summarize. |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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?
No annotations provided, so description must disclose behavioral traits. It only states the output format but does not clarify if it's read-only, required permissions, rate limits, or any side effects.
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?
Description is a single concise sentence that front-loads the verb and resource. No wasted words.
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 presence of an output schema, the description does not need to explain return values. It adequately covers core functionality but could benefit from specifying resource types or statuses. Overall adequate for a simple overview tool.
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 description coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema descriptions for api_key and project_id.
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 it retrieves a resource overview with counts grouped by resource type and status, using specific verb and resource. It effectively distinguishes from sibling tools like get_project or list_project_resources.
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?
No explicit guidance on when to use this tool versus alternatives. Usage is implied from the description, but lacks clear context for when to choose this over list_project_resources or get_project.
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 have distinct purposes, but some pairs like create_dataset vs create_dataset_from_csv or pull_results vs pull_job_csv could cause confusion. However, descriptions clarify differences.
Tools follow a consistent verb_noun pattern (e.g., create_dataset, list_datasets) with minor exceptions like append_csv_to_dataset and pull_job_csv. Overall predictable.
60 tools is high for an MCP server, but the domain (web research, job processing, multiple resource types) justifies the count. Still borders on excessive.
The server offers full CRUD for datasets, entities, monitors, projects, webhooks, plus job submission, status polling, result retrieval (JSON/CSV), webhook management, and health endpoints. No obvious gaps.