run_repository_fix
Run repository pipeline then apply the result, returning the canonical repository envelope.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| apply | No | ||
| pipeline | No | ||
| repository_id | Yes |
Algenta MCP Server
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must fully disclose behavioral traits. It indicates a mutating operation (run and apply) but fails to clarify the nature of the 'apply' step (e.g., destructive, irreversible) or required permissions. The term 'canonical envelope' is not explained. This leaves significant behavioral ambiguity.
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?
The description is a single sentence of 12 words, efficiently conveying the core workflow. It avoids redundancy and is well-structured. However, it is so brief that it sacrifices completeness, but for conciseness alone it is effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (three parameters, two nested objects, no output schema), the description is insufficient. It lacks details on parameter formats, return value structure, and the implications of the 'apply' step. An agent would likely need to infer or experiment to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description should compensate by explaining the parameters. However, it only alludes to 'pipeline' and 'result' without describing the 'apply' and 'pipeline' objects or the meaning of 'repository_id'. This provides no added semantic value beyond a basic operation outline.
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?
The description clearly states the two-step process: running a repository pipeline and then applying the result, with the outcome of returning the canonical repository envelope. This distinguishes it from siblings like 'run_repository_pipeline' (which likely only runs without applying) and 'apply_repository' (which may apply without running). However, it could be more explicit about what 'apply' entails.
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?
No guidance is provided on when to use this tool versus alternatives. It does not mention prerequisites, when not to use it, or potential limitations. Given the diverse set of siblings, this omission makes it difficult for an agent to decide appropriately.
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.