get_limits
Get current plan quotas and limits for the active API key.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Algenta MCP Server
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.7/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It states the tool performs a read operation (get quotas/limits). While adequate, it lacks disclosure of any special permissions, rate limits, or staleness of data.
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 a single clear sentence of 10 words. It is front-loaded and contains no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should explain what is returned. It mentions 'plan quotas and limits' but does not describe the structure or format of the response. However, for a simple getter with no parameters, it is minimally complete.
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?
The tool has no parameters, so the schema coverage is 100% trivially. According to the rubric, 0 parameters yields a baseline of 4. The description adds no parameter info but is not required to.
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 retrieves plan quotas and limits for the active API key, which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'get_usage' by specifying it returns limits rather than usage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives (e.g., 'get_usage'). It does not mention when not to use it or any prerequisites.
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
C2.7/5.0
Disambiguation4/5
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.
Naming Consistency3/5
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.
Tool Count1/5
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.
Completeness4/5
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.