AI Context Manager

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation3/5
Most tools are distinct, but ai_cloud_push and ai_cloud_sync (with push option) overlap, and ai_materialize_documents and ai_sync_environment_docs are very similar. The deprecated ai_cloud_pull adds confusion.
Naming Consistency4/5
All tools use 'ai_' prefix and underscore separation, with a mix of verb_first and noun_first patterns (e.g., detect_environment vs cloud_push). Overall readable and fairly consistent, but not perfectly uniform.
Tool Count5/5
12 tools cover the domain of context management, cloud sync, memory, and environment setup without being overwhelming or sparse.
Completeness4/5
Core workflows (init, sync, memory, session, environment detection) are covered. Minor gaps like memory deletion or cloud asset management are absent but not critical for the stated purpose.
Average 3.3/5 across 12 of 12 tools scored. Lowest: 2.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on May 10, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as side effects (e.g., mirror_delete deletes local files) or permissions needed. The description only says 'download assets' without any behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short but not effectively concise; it is disjointed and contradictory (deprecated yet suggests using cloud API). It fails to front-load key information and wastes space on a confusing recommendation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 6 parameters, no output schema, and no annotations, the description is grossly inadequate. It lacks essential context about behavior, return values, and usage scenarios, making it nearly unusable for proper agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 50%, but the tool description adds no additional meaning beyond what the schema provides. It does not explain or clarify any of the 6 parameters, leaving the agent without semantic guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'DEPRECATED' and 'Descarga assets' but then says to use the cloud API directly, creating confusion about the tool's actual function. It is tautological and misleading rather than clearly stating what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'DEPRECATED' and suggests using the cloud API directly, which is a clear alternative. This provides guidance on when not to use the tool and what to use instead, though it lacks details on specific scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose behavioral traits such as pagination, return format, rate limits, or whether it returns full memory entries or summaries. The description adds minimal value beyond the tool name.
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, concise sentence that front-loads the verb and resource. It is appropriately sized for a simple list tool, though it could add more context without being verbose.
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 no annotations, no output schema, and 0% parameter coverage, the description lacks completeness. It does not cover return values, pagination, or authorization needs, leaving the agent with significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% for all 3 parameters. The description only mentions that these are filters but does not explain their data types, constraints, or behavior. With zero schema coverage, the description should provide more detail.
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 verb 'List' and the resource 'memory entries', and mentions filtering by session_id, project, and tag. However, it does not differentiate from sibling tools like ai_memory_add or ai_memory_materialize_history.
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 implies that filters can be used but provides no explicit guidance on when to use this tool versus alternatives, nor any context on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It indicates a mirror sync (cloud→workspace) and optional push, but fails to explain what 'mirror mode' entails (e.g., conflict resolution, whether it overwrites files, safe to cancel). The description omits critical behavioral context for a sync operation, making it insufficient for safe use.
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 consists of two concise sentences, front-loading the default behavior. It avoids redundancy and wastes no words. However, it could be slightly more precise (e.g., defining 'modo espejo') without adding length. Overall, it is well-structured and efficient.
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 has 3 parameters, no output schema, and no annotations, the description is too brief. It lacks details on return values, error handling, behavior when no workspace is specified, and what happens to local files. For a sync operation, more completeness is needed to ensure safe invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 2 of 3 parameters with descriptions (dry_run, push). The description adds context for the push parameter by linking it to the optional local-to-cloud sync. However, the workspace parameter has no description in either schema or description, reducing overall semantic value. With 67% schema coverage, the description partially compensates but leaves gaps.
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 states it syncs cloud to workspace in mirror mode by default, with optional local push. This clearly identifies the tool's primary action and distinguishes it from siblings like ai_cloud_pull and ai_cloud_push. However, the description is in Spanish, which may reduce clarity for English-speaking agents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for default mirror sync from cloud to workspace, with optional push. It provides context on default behavior but does not explicitly state when to use this tool versus alternatives like ai_cloud_pull (for one-way pull) or ai_cloud_push (for one-way push). No exclusion criteria 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the basic upload action without mentioning overwrite behavior, authentication needs, rate limits, or effects on existing data. This is insufficient for safe invocation.
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?
Two sentences, no waste, front-loaded with the main action. Every word is necessary for conveying the core purpose.
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?
For a tool with 4 parameters, no output schema, and no annotations, the description omits key details: behavior for 'type=all', interaction between 'slug' and other params, and expected return values or errors. The presence of overlapping siblings (ai_cloud_sync) further highlights the need for more context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds value for the 'type' parameter by listing the asset categories (skills, prompts, specs) that map to its enum values. However, it does not explain the 'slug', 'project_slug', or 'workspace' parameters beyond what the schema already provides. With 50% schema coverage, the description compensates partially but not fully.
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 action ('Publica' meaning publishes) and the resource (local assets to StandarCloud, specifically skills, prompts, and specs). Although sibling tools exist, the name and description distinguish it as the upload counterpart to ai_cloud_pull and ai_cloud_sync.
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 its siblings like ai_cloud_sync (which might handle bidirectional sync) or ai_cloud_status. It lacks context on prerequisites, exclusions, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as side effects, required permissions, or idempotency. The only behavioral hint is that the tool detects the environment and returns one of the listed values, but deeper transparency is lacking.
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 extremely concise: a single phrase that conveys the tool's function. No wasted words, and the core information is front-loaded.
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 the tool's simplicity (no parameters, no output schema, no annotations), the description is minimally adequate. It lists the possible environment values but does not explain the return format or error handling. For a straightforward detection tool, this may suffice, but there is room for more completeness.
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 zero parameters, and the input schema coverage is 100% (vacuous). The description does not need to add parameter info, and the baseline score for 0 parameters is 4.
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 tool's purpose: detecting the current environment, listing possible values (claude, vscode-copilot, opencode, unknown). This is a specific verb and resource, though it does not explicitly differentiate from sibling tools. However, no other sibling tool appears to perform environment detection, so the purpose is sufficiently clear.
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. There is no mention of prerequisites, context, or exclusions. The tool's purpose is implied to be informational, but explicit usage guidelines are absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description does not disclose mutation behavior, permissions, or side effects beyond the implied write operation.
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?
Single sentence, no fluff. Every element (verb, resource, tag constraint) is purposeful.
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?
No output schema; description does not mention return value or behavior after add. With 5 params and nested objects, more context is needed for full understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 60%; description adds the tag enum values and reinforces required params, but does not elaborate on metadata or session_id format beyond schema.
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?
Clearly states the verb 'adds' and resource 'memory entry to a session'. Mentions the tag taxonomy, distinguishing it from sibling tools like ai_memory_list.
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 on when to use this tool versus alternatives like ai_memory_list or ai_memory_materialize_history. Lacks explicit context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes fetching from Django and materializing, plus auto-import for opencode. No annotations exist, so description carries full behavioral burden. Lacks info on idempotency, error handling, or side effects like deletion. Moderate transparency.
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?
Single sentence conveys purpose and output formats efficiently, no redundancy. Could be slightly better structured by breaking into bullet points, but acceptable.
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?
No output schema; description does not explain return values or behavior of parameters like max_entries, external_id, workspace, or environment. Incomplete for a 7-parameter tool with no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% so baseline is 3. Description adds minimal extra meaning beyond schema (e.g., 'desde la web (Django)' adds context for project parameter). No significant new parameter insights.
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?
Description clearly specifies verb 'materializa' (materializes) and resource 'histórico de sesión' from web (Django) and output formats (prompt file for vscode-copilot/claude, JSON for OpenCode). Distinguishes from sibling tools like ai_memory_list or ai_cloud_pull.
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 explicit guidance on when to use this tool vs alternatives, no when-not-to-use or exclusion criteria. Siblings are listed but no comparative advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behavior: detects environment, fetches documents from API, and materializes locally. However, does not describe side effects (e.g., overwriting local files), safety profile (destructive vs. non-destructive), or idempotency. With no annotations, the description should provide more detail about these behavioral traits.
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?
Two concise sentences that front-load the action and purpose. No unnecessary words or repetition. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (1 optional param, no output schema, no nested objects), the description covers the main action adequately. However, it lacks details about return values, prerequisite environment detection, or what 'materializes locally' exactly entails. Slightly incomplete but sufficient for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'force' is fully described in the input schema with a clear meaning. The overall description adds context by explaining the tool's purpose, but does not add additional semantic meaning beyond the schema. Baseline 3 is appropriate given 100% schema coverage.
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 tool syncs native documents from cloud to local for the current environment, using specific verbs like 'Sincronizar', 'Detecta', 'obtiene', and 'materializa'. It does not explicitly differentiate from sibling tools like ai_cloud_sync or ai_cloud_pull, but the environment-specific focus is implied.
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 provided on when to use this tool versus alternatives. The description does not mention prerequisites, conditions for use, or when not to use it. Lacks any explicit instructions for context-aware selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It only reveals the API token requirement, but omits critical behaviors like whether it overwrites files, destroys data, or has side effects. This is insufficient for safe invocation.
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 extremely concise: one sentence for purpose and one for a prerequisite. It is front-loaded and contains no fluff. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description adequately covers what the tool does, the target documents, and an authorization requirement. It could mention return behavior or error cases but is fairly complete given the context.
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 schema covers 100% of the parameter structure, but the description adds semantic value by explaining the purpose of the documents (native environment files like Claude.md) and the API token requirement. This clarifies the parameter's role beyond the schema's structural description.
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 tool's action ('materializar documentos nativos') and identifies specific target files (Claude.md, .github/copilot-instructions.md). It distinguishes from sibling tools by focusing on environment-specific document materialization, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (current environment documents) and mentions a prerequisite ('Requiere token de API'), but it does not specify when to avoid using this tool or compare with alternatives like ai_detect_environment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. 'Muestra' (shows) indicates a non-destructive read action. Adequate for a status tool.
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 sentence that immediately conveys the tool's purpose without unnecessary words.
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?
The description explains what the tool does but omits details on parameter filtering, output format, or error conditions. For a simple status tool, it is adequate but not comprehensive.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single optional parameter 'workspace' is not mentioned in the description, and schema coverage is 0%. The description adds no value for parameter usage, leaving the agent to guess its purpose.
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 that the tool shows synchronization status, with specific details on local vs remote assets and pending push/pull. This distinguishes it from siblings like ai_cloud_push and ai_cloud_pull that perform actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for checking status but does not explicitly state when to use versus alternatives. Sibling context suggests it's for status, but no direct guidance on when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral traits. It mentions creation and reuse but omits key details: idempotency, side effects on the project, return value structure, authentication needs, or error conditions. For a mutating tool that creates/reuses resources, significant transparency gaps remain.
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 sentence in Spanish that concisely states the primary behavior (create/reuse) and the conditional reuse logic. No wasted words; the action is front-loaded with the verb. Perfect conciseness for the information provided.
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 the absence of an output schema and annotations, the description does not explain the return value (e.g., session ID, object, boolean) or error scenarios. It adequately describes input parameters and core behavior but leaves the agent guessing about what to expect as output, which is a notable gap for a tool that creates or retrieves a resource.
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?
Schema coverage is 100%, baseline is 3. The description adds meaning beyond the schema by explaining 'project' is a cloud slug, 'external_id' is recommended as an external conversation/run ID, and 'title' is optional. This provides useful context for parameter usage.
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 creates or reuses a session in the cloud backend (Django) associated with a Project, and explicitly specifies the reuse condition (same external_id for that project). This is a specific verb+resource combination and is well-differentiated from sibling tools, none of which target session management.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: when a session needs to be created or reused for a project with an optional external_id. It does not explicitly state when not to use or list alternatives, but the context is sufficient given no sibling tools overlap in functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behaviors: if project_slug is omitted, it lists projects and guides the user; if provided, it creates .acm/ and writes project.json. However, it does not mention side effects like network calls or user interaction details.
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 two sentences long, front-loaded with the main action, and contains no unnecessary words. It efficiently conveys both modes of operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main initialization workflow, but could mention next steps (e.g., using ai_cloud_pull/push) or prerequisites. Given no output schema, it is reasonably 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?
With 100% schema coverage, the description adds value by explaining the conditional behavior of project_slug. It also confirms defaults for workspace and force, enhancing understanding beyond the schema.
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's purpose: initializing a workspace with .acm/ directory and linking it to a StandarCloud project. It distinguishes between two modes based on whether project_slug is provided, which aligns well with the tool name and differentiates it from sibling tools like ai_cloud_pull/push.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (initialization), but lacks explicit guidance on when not to use it or alternatives. While sibling tools imply this is for setup, the description could mention subsequent sync tools.
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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