Atlas MCP Server

Server Quality Checklist

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

Latest release: v1.0.29
Disambiguation2/5
Many tools have overlapping purposes, such as atlas_critique, atlas_review, and atlas_self_validator all providing code quality analysis, and atlas_error_predictor and atlas_bug_oracle both predicting bugs. While descriptions are detailed, agents would struggle to reliably select the correct tool for a given task due to these unclear boundaries.
Naming Consistency2/5
All tool names share the 'atlas_' prefix, but the remainder is inconsistent: some use verbs (atlas_test, atlas_decompose), others nouns (atlas_context, atlas_git), and many are multi-word noun phrases (atlas_codebase_surgeon, atlas_tech_debt_quantifier). This mixed convention makes it hard to predict tool names from their function.
Tool Count2/5
With 38 tools, the server is heavily overloaded. While it aims to cover a broad coding assistant domain, the sheer number exceeds what is coherent for an MCP server, and many tools could be merged or removed without losing capability. This count falls into the 'too many' category (25+).
Completeness4/5
The tool set covers a wide range of coding assistance features: testing, debugging, refactoring, documentation, security, performance, UI design, and more. There are minor gaps, such as no direct tool for running project tests or executing git operations, but the overall surface is quite comprehensive for its stated purpose.
Average 3.5/5 across 38 of 38 tools scored. Lowest: 2.6/5.
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
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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

Behavior1/5
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 merely states the tool 'provides confidence scoring' without explaining output format, how scores are computed, whether verificationData is required, or any other behavioral traits. This is extremely minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no fluff, but the second sentence is somewhat redundant with the first ('helps understand reliability' restates the purpose). It is concise but not tightly structured with front-loaded specifics.
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?
The tool has a nested object parameter and no output schema, so the description must explain what the tool returns. It does not, instead remaining at a high level. The schema covers parameters well, but the description is missing essential return value and usage 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?
Schema description coverage is 100%, so the schema already documents all four parameters. The description adds no additional parameter semantics beyond mentioning 'code suggestions, solutions, and responses,' which loosely maps to the suggestion parameter. This meets the baseline 3.
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 provides confidence scoring for code suggestions, solutions, and responses, which is a specific function. However, it does not distinguish this tool from sibling tools like atlas_critique or atlas_review, so it falls short of a 5.
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?
There is no explicit guidance on when to use this tool versus alternatives. The phrase 'helps understand reliability and certainty of recommendations' implies a use case, but no exclusions or alternative tool references are provided.
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 must fully disclose behavioral traits. It mentions git integration and confidence intervals, but omits output format, side effects (e.g., whether it modifies files), prerequisites, or how it behaves on large projects. This leaves significant behavioral gaps.
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 8-bullet capabilities list is verbose and redundant, with items like 'Predict where bugs are likely to occur' and 'Identify high-risk code changes' overlapping. The key differentiator is buried at the end, and the description could be condensed to a few key points.
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 exists, so the description should explain what the tool returns and how to interpret scores. It does not describe the return format, example usage, or operational constraints, leaving agents without enough context to successfully invoke and use the 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?
Schema descriptions cover all 6 parameters, giving a baseline of 3. The description adds related context like 'confidence intervals' and 'git history for churn analysis' which aligns with analysisDepth and includeGitHistory, but does not meaningfully enhance parameter understanding beyond the schema.
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 identifies the tool as a predictive bug detection and risk analysis engine, with explicit capabilities like 'Predict where bugs are likely to occur' and 'Calculate bug probability scores for each file/function'. It distinguishes itself from static analyzers but does not mention sibling tools like atlas_error_predictor, so differentiation is partial.
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 for predicting future bugs rather than static analysis, stating 'This is NOT a static analyzer - it predicts FUTURE bugs using ML-like heuristics.' However, it does not provide explicit when-to-use or alternatives, leaving the user to infer the intended context.
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 mentions what is analyzed (files, dependencies, project structure) but does not disclose read-only nature, potential limitations, or behavior regarding inputs like query or filePaths.
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 concise sentences, front-loaded with the main verb. Every word earns its place, making it easy to parse quickly.
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 lack of output schema and annotations, the description is too brief. It does not explain return value, usage scenarios, or how this tool differs from the many sibling tools, leaving an agent under-informed.
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 the baseline is 3. The description adds minimal meaning by referencing project structure, which aligns with projectPath, but it does not elaborate on query or filePaths beyond the schema's own descriptions.
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 uses a clear verb ('gather context') and names specific resources ('files, dependencies, and project structure'). It effectively states what the tool does, though it does not explicitly differentiate from sibling tools like atlas_dependencies or atlas_explain.
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, no exclusions or conditions are mentioned. The description implies a general use case but lacks any contextual direction for an agent.
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 full burden for behavioral disclosure. It only states high-level behavior (automatically determines, provides relevance scoring) without mentioning potential side effects, data access, performance implications, or limitations. It is not misleading but adds little behavioral clarity.
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 clear, front-loaded sentences with no redundancy. Every word contributes to describing the core purpose and output. This is appropriately concise.
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?
The tool is conceptually complex (analysis of a codebase, relevance scoring, output suggestions), but the description does not explain the output format, what 'optimal context' looks like, whether the tool is read-only, or how it uses the projectPath. With no output schema and no annotations, this description leaves significant gaps for an agent.
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 all parameters are documented in the input schema. The description adds a general notion of relevance scoring but gives no specific parameter-level insight beyond what the schema provides. Baseline 3 is appropriate.
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 determines relevant files, functions, and context for a given task, with a specific verb ('determines') and resource scope. It does not explicitly differentiate from closely related siblings like atlas_context or atlas_semantic_search, which prevents a 5.
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 use when a task needs context prioritization, but gives no explicit when-to-use guidance, no exclusions, and no mention of alternative tools. For a sibling-heavy environment, this is insufficient guidance.
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 of behavioral disclosure. It only states that it 'returns detailed feedback and scores' but does not clarify side effects (e.g., read-only nature), authentication requirements, rate limits, or any constraints. The description lacks transparency about what the tool actually does beyond returning output.
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, front-loaded with the core purpose, and includes a brief mention of the output type. It is concise, avoids fluff, and every word contributes to understanding the tool's function.
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?
The tool has no annotations and no output schema, so the description must compensate. It explains the basic purpose and that it returns feedback and scores, but omits important context such as response format, input size limits, how 'focus' affects output, and how this tool differs from similar review tools. For a critique tool with sibling overlap, this is incomplete.
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 already provides 100% coverage of all three parameters with descriptions. The tool description does not add meaningful parameter-level information beyond what the schema offers; it only mentions 'quality, security, and best practices,' which loosely relate to the 'focus' parameter but do not explain usage or syntax. Since schema coverage is high, the baseline of 3 is appropriate.
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: review and critique code for quality, security, and best practices, and mentions it returns feedback and scores. The verb 'review' and resource 'code' are specific. However, it does not distinguish from sibling tools like atlas_review or atlas_security, which likely overlap in function.
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. It does not mention exclusions, prerequisites, or situations where other tools might be more appropriate. Usage is only implied by the generic purpose statement.
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 behavior. It mentions the output ('Creates a DAG of work items') but does not clarify whether this is a read-only operation, whether it modifies state, requires external calls, or has side effects. The description lacks transparency about the tool's operational footprint.
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, front-loaded with the core action ('Break down a complex task') and includes the key output concept. Every word earns its place; there is no redundancy or unnecessary elaboration.
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 lack of annotations and output schema, the description is minimally adequate for a simple planning tool. It states the core function and output, but it omits usage context and behavioral details that would help an agent decide when to invoke it. It is not incomplete enough to be a 2, but lacks the richness of a well-rounded description.
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% (all three parameters have descriptions in the schema), so the baseline is 3. The description itself adds no parameter-specific nuance beyond the schema, but the schema already documents each parameter adequately. This meets the baseline without further enhancement.
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 identifies the tool's primary function with a specific verb and resource: 'Break down a complex task into smaller subtasks with dependencies.' It also specifies the output form ('Creates a DAG of work items'), which adds clarity. However, it does not explicitly distinguish itself from sibling tools by name or contrast, so it lacks full sibling differentiation.
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?
There is no guidance on when to use this tool versus alternatives. The description only states what the tool does, without any mention of prerequisites, suitable scenarios, or exclusions. It does not reference any sibling tools or suggest when decomposition is appropriate versus other atlas operations.
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 full responsibility for behavioral disclosure. It mentions that it extracts specific elements, which hints at output, but does not explain how it handles missing input, whether conversationHistory is used for context, or what the return format looks like. It omits any limitations or 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main action, and contains no filler. Every sentence adds information about the tool's purpose and output extraction.
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?
For a tool with two parameters and no output schema, the description provides enough to understand the basic intent analysis purpose. However, it lacks details on optional conversationHistory usage, expected output structure, or error conditions, making it minimally complete.
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%, with both parameters (message and conversationHistory) described in the input schema. The description adds no additional meaning about parameters, so it meets the baseline but does not exceed it.
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 function: 'Analyze user intent from a natural language request' and specifies the extraction of action type, target, context, and constraints. This is a specific verb+resource combination, but it does not explicitly differentiate from sibling tools like atlas_decompose or atlas_context that might also involve analysis or intent.
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?
There is no guidance on when to use this tool versus alternatives. The description only states what it does, not when it should be preferred. It provides no exclusions or mention of sibling tools, leaving the agent to infer usage context.
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 of behavioral disclosure. It lists capabilities like tagging and semantic search but does not disclose operational traits such as the destructive nature of the 'clear' action, overwrite behavior, or persistence guarantees. The agent has no warning about irreversible effects.
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, front-loaded with the primary purpose, and every word adds value. There is no redundancy or filler.
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?
Despite having 5 actions and 8 parameters, the description only touches on store/retrieve and a few features. It fails to mention the 'list' and 'clear' actions, what return values look like (no output schema), or the implications of clearing memory. The agent is left without critical context for safely using all actions.
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%, providing a baseline of 3. The description adds value by explicitly calling out 'tagging, importance scoring, and semantic search,' which maps to the tags, importance, and query parameters, thus reinforcing their meaning beyond the schema's property descriptions.
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 a specific verb+resource: 'Long-term memory storage across sessions' and 'Store and retrieve important context, decisions, patterns, and learnings.' It communicates the core function and distinguishes it as a persistent memory tool, though it does not explicitly differentiate from sibling tools like atlas_semantic_search.
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?
There is no explicit guidance on when to use this tool versus alternatives, nor any exclusions or conditions. The phrase 'across sessions' implies a use case, but the description does not tell the agent when to prefer this over atlas_context or atlas_semantic_search, nor when to avoid 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?
With no annotations, the description carries full burden for behavioral disclosure. It mentions the tool 'provides grades, findings, and suggestions,' which describes outputs, but it does not state whether the tool is read-only, what side effects exist, or any permission requirements. For a review tool, non-destructive behavior is implied but not explicitly stated.
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, both informative and free of filler. It front-loads the core purpose and immediately lists the analysis dimensions and output types, achieving maximum clarity in minimal space.
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 complexity (13 parameters, no output schema, no annotations), the description is adequate but not complete. It explains what the tool does and what it returns, but it does not mention required inputs (code, language) beyond what's in the schema, nor does it describe the format of grades or findings. Some context is inferred from the tool name and 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 description coverage is 100%, so baseline is 3. The description lists the check dimensions (quality, security, performance, etc.) which map to the boolean parameters, adding slight context. However, it does not elaborate on parameter usage or relationships beyond what the schema already provides.
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 is an AI code review assistant with multi-dimensional analysis (code quality, security, performance, etc.) and provides grades, findings, and suggestions. However, it does not explicitly distinguish itself from sibling tools like atlas_security or atlas_critique, which may overlap in function.
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 versus alternatives. The description implies it is for code review, but there are no exclusions, prerequisites, or references to specialized sibling tools. For a tool with many similar siblings, this lack of differentiation is a gap.
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?
The description mentions 'Dry-run mode for safe testing' and 'Auto-rollback generation,' which are positive behavioral disclosures. However, it fails to convey the autonomous, potentially destructive nature of the tool, and claims like '100% accuracy' are misleading. With no annotations, the description should more clearly warn about the risk of large-scale mutations and the need for validation. This is a significant transparency gap.
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 overlong and full of marketing hype ('Revolutionary capabilities far beyond basic refactoring'). While the title is front-loaded, the bullet list contains repetitive claims and unsupported superlatives. A concise paragraph stating the core functionality and key parameters would be more 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?
With no output schema and a nested operation object that lacks type-specific parameter definitions, the description should explain return values, error handling, and the full scope of each operation. It only vaguely references features like 'blast radius analysis' without explaining what the agent can expect. The lack of information about the tool's output and operation details makes it incomplete for a complex tool.
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 description coverage is 100%, so baseline is 3. The description adds value by explaining several operation types (e.g., 'semantic code transplant,' 'cross-file rename') through its capability list, giving more context to the operation enum. However, it does not detail the type-specific parameters for each operation, so it does not fully compensate for the vague nested operation schema.
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 identifies the tool as a multi-file refactoring engine with a specific verb and resource ('AUTONOMOUS MULTI-FILE SURGICAL REFACTORING ENGINE'). It lists concrete operation types and scopes to multi-file changes, which distinguishes it from simpler refactoring tools. However, it does not explicitly contrast itself with the sibling atlas_refactor, so differentiation relies on the multi-file emphasis.
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?
It explicitly states 'Use this for surgical precision code modifications that affect multiple files,' which tells the agent when to select this tool. It does not mention alternatives or exclusion criteria, but the context is clear enough for basic selection. The lack of a 'when not to use' statement prevents a 5.
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 explaining behavior. It only says 'Applies improvements while maintaining functionality', which indicates a non-breaking write operation but omits key behavioral traits like whether the code is modified in-place, what output is returned, whether tests are run, or any side effects. This is insufficient for a mutation 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 exactly two sentences, front-loaded with the primary purpose ('Optimize code'), followed by a concise behavior statement. Every word earns its place; there is no fluff or redundant information.
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 that there are no annotations and no output schema, and the tool has 4 parameters with meaningful options like 'preserveApi' and 'language', the description is under-specified. It does not explain what the tool returns, how the parameters interact, or whether the optimization is one-time or iterative. An agent would lack key context to confidently invoke the 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 input schema has 100% description coverage for all 4 parameters, so the baseline is 3. The description does not add extra meaning to the parameters; it only indirectly references 'critique feedback' which aligns with the 'critique' parameter but does not elaborate on 'language' or 'preserveApi'. Therefore, the description provides no added semantic value beyond the schema.
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 uses a specific verb 'optimize' with a clear resource ('code') and adds the trigger 'based on critique feedback', which helps distinguish it from siblings like atlas_refactor. It clearly states the tool's main action, though 'optimize' is somewhat broad and could be more specific about the type of improvements.
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 phrase 'based on critique feedback' implies when to use the tool (after receiving critique), but it does not explicitly state when not to use it or compare to alternatives such as atlas_refactor or atlas_debug. There are no exclusions or alternative tool references, so the guidance is only implied.
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 of behavioral disclosure. It mentions 'learns and adapts,' implying stateful behavior, but is vague about side effects, persistence, permissions, or return outcomes. It does not explain how learning occurs, whether it modifies stored knowledge, or what the agent should expect after calling the 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 concise, only two sentences, and front-loaded with the key purpose. Every sentence contributes to explaining what the tool does, with no redundant or filler content.
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 five parameters and four distinct actions (learn, apply, suggest, report), the description is too high-level. It does not clarify what the different actions do, what output to expect, or how the tool integrates with the codebase, especially given the absence of an output schema. Significant gaps remain for an agent to invoke it correctly.
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 the baseline is 3. The description adds some context by mentioning coding patterns and style preferences, which loosely align with the 'aspects' enum, but it does not elaborate on parameters like action, code, or projectPath beyond the schema descriptions. It adds marginal value.
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 'learns and adapts to coding patterns, style preferences, and architectural choices' and 'provides style-consistent suggestions,' making its primary function evident. It distinguishes itself from sibling tools by focusing on pattern learning and style adaptation rather than actions like refactoring or code review, 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 when the user needs style-consistent suggestions based on codebase patterns, but it does not provide explicit when-to-use conditions or contrast with sibling tools. There is no mention of when not to use it or prerequisites, leaving the guidance to inference.
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 of disclosing behavioral traits. It only lists the pipeline stages, omitting any mention of side effects such as git operations, code modifications, or persistent changes. The 'Git' stage hints at side effects but no specifics are given.
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 that front-loads the primary action and lists the pipeline stages efficiently. It is concise and easy to scan, though the brevity sacrifices behavioral detail.
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 complex pipeline tool with no annotations and no output schema, the description is incomplete. It does not explain return values, how options affect execution, what happens to the project, or failure modes. Significant context is missing.
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 input schema provides descriptions for 'message' and 'projectPath' (67% coverage), but the nested 'options' object and its properties (skipGit, numVariants, maxDecomposeDepth) lack descriptions. The tool description adds no parameter context, leaving these options ambiguous beyond their names.
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 'Run the full Atlas pipeline' and enumerates the stages, making it evident this is a composite orchestration tool. This distinguishes it from the individual atlas_* sibling tools like atlas_intent or atlas_git.
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 for running the entire pipeline rather than individual stages, but it does not explicitly state when to use this tool versus the stage-specific siblings. No exclusions or alternative recommendations are provided.
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 of disclosing behavioral traits. It does not state that the tool modifies the input code, what the output format is, whether behavior is preserved, or any side effects or prerequisites. The description only lists high-level capabilities and omits critical operational 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 succinct sentences, front-loaded with the core purpose and immediately enumerating key features. Every phrase adds value without redundancy or irrelevant detail.
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?
This is a high-complexity tool with 11 parameters and no output schema, so the description should explain return values or operational outcomes. It does neither; it fails to mention what the user gets after refactoring, whether it's a diff, rewritten code, or suggestions. The clear param schema helps, but the missing output/side-effect context makes it incomplete.
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 schema provides 100% parameter coverage, so the baseline is 3. The description mentions concepts like complexity and SOLID principles which map to parameters, but it does not add new meanings or clarify defaults beyond what the schema already states.
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 identifies the tool as code refactoring with pattern detection, complexity reduction, SOLID enforcement, and modernizations. It distinguishes itself from sibling tools by listing these refactoring-specific capabilities, though it lacks an explicit action verb like 'Refactors' and doesn't directly contrast with siblings.
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 for code smells, design patterns, and modernization, but does not provide explicit when-to-use/when-not-to-use guidance or mention alternatives like atlas_decompose or atlas_optimize. The context is clear enough but the lack of explicit exclusions or alternatives leaves a gap.
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?
The description discloses the tool's behavior as a learning, prediction engine that analyzes codebase patterns and generates style-matched code. It does not mention side effects, permissions, output format, or limitations, and with no annotations provided, the description only partially carries the transparency burden. The reader cannot infer if it mutates files or requires external services, but the core behavior is reasonably clear.
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 a long, bullet-pointed list of marketing claims, many of which are redundant (e.g., 'context-aware', 'multi-file aware', 'understands your project's DNA'). It is not concise and could be condensed into a few key sentences without losing meaning.
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 complex tool with 9 parameters, 5 modes, and nested objects, the description is incomplete. It provides no guidance on how to use the modes, what the response format is (no output schema), or how to structure a valid request. The high-level feature list does not help an agent invoke the tool correctly.
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 already provides detailed descriptions for most parameters (89% coverage), so the description's lack of parameter-specific guidance is acceptable. The description's mention of 'natural language to code translation' hints at the intent parameter, but it adds little meaning beyond what the schema already provides.
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 identifies the tool as an intelligent code completion and prediction engine, with specific capabilities like predicting entire code blocks and multi-file aware completions. It states a clear purpose ('Use for intelligent code generation...') and the feature list distinguishes it from basic autocomplete. However, it does not explicitly differentiate from sibling tools, so it earns a 4 rather than a 5.
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 includes a direct usage directive ('Use for intelligent code generation that understands your project's DNA') and lists several contexts such as context-aware and intent prediction. However, it does not explain when not to use the tool or how to choose among the five schema modes (complete, generate, transform, explain, fix), so it lacks the explicit exclusions or alternatives needed for a 5.
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 carry the full burden of behavioral disclosure. It lists capabilities but does not mention side effects, whether files are modified, required permissions, or output format. For a tool with multiple actions, this is a significant transparency gap.
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 that efficiently enumerates the tool's major capabilities without wordiness. While it packs multiple verbs, it remains concise and informative, though slightly dense.
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?
With 5 parameters, no output schema, and no annotations, the description must provide more context for correct invocation. It fails to explain the return value structure, prerequisites, or limitations, offering only a high-level summary. This is incomplete for a tool of this complexity.
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?
All parameters have descriptions in the schema (100% coverage). The description adds some context by linking concepts like 'converts between CSS methodologies' to the targetMethodology parameter, but this is marginal since the schema already documents the enums and purpose. Baseline 3 is appropriate.
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 identifies the tool as a CSS architecture analyzer with specific functions: detecting specificity conflicts, generating design tokens, converting between CSS methodologies, and providing refactored CSS. It uses specific verbs and resources, and distinguishes itself from sibling tools by focusing exclusively on CSS.
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 for CSS analysis and refactoring tasks, but it does not explicitly state when to use this tool versus alternatives like atlas_refactor or atlas_ui_ux_designer. No exclusions or alternative guidance are provided, leaving usage partially inferred.
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 of behavioral disclosure. It states the tool 'creates' various documentation artifacts but does not reveal whether it modifies files, returns strings, requires network access, or has side effects. This lack of behavioral detail is a meaningful gap for a tool that may alter the codebase or produce external files.
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 core purpose, and every phrase adds value. It avoids redundancy and is highly scannable.
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?
With no output schema or annotations, the description should clarify what the tool returns and how it behaves. It covers basic capabilities but omits critical context such as whether it overwrites files, how auto-detection works, or what the output structure looks like. The 100% schema coverage mitigates parameter ambiguity, but overall the description leaves notable gaps.
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 already documents all 7 parameters with descriptions, achieving 100% coverage. The tool description adds minimal parameter-related insight, such as mentioning 'usage examples' which maps to includeExamples, but does not enrich understanding of the style or format enums beyond what the schema provides. Baseline 3 is appropriate.
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 opens with 'Generate documentation for code,' a specific verb+resource pairing. It further elaborates with concrete outputs (JSDoc/TSDoc/PyDoc comments, README files, API documentation, usage examples), clearly distinguishing it from sibling tools like atlas_explain or atlas_refactor.
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. It does not mention prerequisites, contexts where documentation generation is appropriate, or contrast with sibling tools. Users are left to infer usage from the name and basic purpose.
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 does not state that the tool is read-only, what happens on no results, how similarity scoring works, or whether any setup or indexing is required. It only lists features, not operational behavior or side effects, leaving the agent uncertain about the tool's execution characteristics.
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 excessively verbose and repetitive, with bullets like 'Search by meaning, not just text matching' and 'Search code by MEANING, not just keywords' saying essentially the same thing. It is more of a marketing pitch than a concise tool description, wasting valuable tokens. The structure is organized but the content is inflated.
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 6 parameters including a nested filters object, and no output schema, the description should explain what the results look like and any limitations. It does not mention the return format, result fields, or any behavior like pagination or sorting. The schema covers parameters, but the behavioral and output context is missing, making the description incomplete for an agent to fully understand the 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?
Schema description coverage is 83%, with all parameters (query, filters, queryType, maxResults, projectPath, includeContext) already well-documented in the input schema. The description does not add any extra parameter-level details, so it adds no value beyond what the schema provides. Baseline 3 is appropriate since the schema does the heavy lifting.
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 identifies the tool as a semantic code search engine with a specific verb ('search') and resource ('code'). It distinguishes itself from keyword-based search by emphasizing meaning-based matching, natural language queries, and intent discovery. This differentiates it from sibling tools like atlas_intent or atlas_context, which likely focus on other aspects.
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 you need to find code by meaning or behavior description, with examples like 'find all authentication code'. It subtly implies it's for semantic search rather than exact matches, but it does not explicitly mention when not to use it or name alternative sibling tools. This is a minor gap.
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 must carry the full burden of behavioral disclosure. It lists generated artifacts but does not state whether the tool writes files, requires authentication, or has side effects. This ambiguity leaves the agent unsure of the tool's runtime impact.
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 with a clear serial list of features. It is relatively concise and front-loads the purpose, though it could benefit from better structure (e.g., bullets) to avoid a run-on feel.
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 tool is complex with 8 parameters and no output schema, but the description only provides a high-level feature list. It does not explain how endpoints, baseUrl, and library interact, nor what the final generated output looks like, leaving gaps for a tool of this complexity.
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 description coverage is 100%, so the parameter baseline is 3. The description repeats some parameter concepts (e.g., types, mocks, Zod schemas) but adds no new parameter-level meaning beyond what the schema already provides.
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 identifies the tool as an 'API integration helper' and lists specific outputs: TypeScript types, React Query/SWR hooks, mock data, API clients with interceptors, and Zod schemas. This specific verb-resource pairing distinguishes it from the many sibling tools focused on analysis or refactoring.
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 the tool is used for API integration code generation, but it provides no explicit when-to-use, when-not-to-use, or alternative tool references. It lacks contextual guidance for choosing this tool over siblings.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does mention accessibility support, adding useful context, but it fails to clarify what the tool returns (e.g., code snippets vs. files), whether it has side effects, or any safety characteristics. This is acceptable for a generator but leaves room for improvement.
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 concise and well-structured: two sentences front-load the primary purpose and then detail the scope. Every phrase earns its place, with no redundant words or unnecessary detail, making it easy for an agent to quickly grasp the tool's function.
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 moderate complexity (7 parameters, 3 required, 4 enums) and the lack of output schema or annotations, the description provides a solid domain overview but omits critical context like required parameters or expected return format. The schema covers parameters, but without an output schema, mentioning the return type would enhance completeness.
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 provides 100% coverage of all 7 parameters, including enums and descriptions, so the baseline is 3. The description reinforces this by listing animation types and libraries that map directly to schema enum values, but it adds no new parameter-specific information beyond what the schema already states.
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 is a 'Professional animation generator' with a specific verb ('creates') and resource (CSS animations, Framer Motion code, GSAP timelines, and micro-interactions). It enumerates the supported animation types (entrance, exit, hover, loading, scroll, gesture), making the purpose highly specific and distinguishable from sibling tools like atlas_css_wizard or atlas_ui_ux_designer.
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 offers no guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. It simply describes capabilities without contextualizing them against sibling tools, leaving the agent to infer usage scenarios without explicit support.
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?
With no annotations, the description carries the burden of explaining behavior. It does disclose the types of analysis performed (null refs, type mismatches, race conditions) and the 'before execution' timing, which is useful. However, it omits what the output looks like, how results are returned, or any caveats about accuracy or limitations, leaving some 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose ('Predicts potential runtime errors...'), and every sentence adds value. There is no fluff or repetition.
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 covers the tool's core function and error categories, but lacks information about the output schema or return format. Since there is no output schema, the description could have explained what the prediction results look like (e.g., list of issues, severity levels). Given the complexity of a prediction tool, this is a notable gap.
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 all four parameters are described in the schema. The description adds no extra parameter details beyond what is already in the schema; it merely restates examples of checkFor values ('null refs', 'type mismatches', 'race conditions'). This meets the baseline but does not exceed it.
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 a specific action ('Predicts potential runtime errors, edge cases, and failure scenarios') with a defined resource ('code execution'). It lists concrete error types ('null refs, type mismatches, race conditions'), which distinguishes it from more general sibling tools like atlas_debug or atlas_review, though it does not explicitly name those 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 phrase 'before code execution' gives implied usage timing, but there is no explicit guidance on when to choose this tool over siblings or when not to use it. Alternatives like atlas_debug or atlas_bug_oracle are not mentioned, leaving the agent to infer based on the name and description.
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?
Annotations are absent, so the description must disclose behavioral traits, and it does not. It describes capabilities (calculating, estimating, predicting) but does not state whether the tool modifies files, requires permissions, or is read-only. There is no mention of side effects or output behavior beyond vague 'reports', leaving the agent without essential operational expectations.
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 well-structured with a bold title and a bullet list, and it front-loads the core purpose. Some bullets are redundant (e.g., 'Calculate actual monetary cost' repeats the first line), but overall it is concise and scannable.
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 complex tool with six parameters and no output schema, the description lacks critical details about return values, report structure, and expected use flow. It promises 'executive-level financial reports' but does not specify whether output is a summary, JSON, file, or interactive output. The features are listed but not tied to how they are delivered.
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 has 100% description coverage for all six parameters, so the schema already documents each parameter. The description's feature list (e.g., 'includeProjections' aligns with 'future debt projections') provides some contextual mapping, but it does not add parameter-specific syntax or format details beyond what the schema provides.
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 identifies the tool as a financial tech debt analysis engine with the specific verb 'Calculate' and resource 'actual cost of technical debt in dollars'. It enumerates distinct capabilities (priority ranking, ROI, projections) that differentiate it from sibling tools which have no analogous financial analysis focus.
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 by stating the tool produces executive-level financial reports to justify refactoring budgets, implying the use case for cost quantification. It does not explicitly name alternative tools or exclusion scenarios, but the context is sufficient for an agent to infer appropriate 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 must disclose behavior fully. It states the tool generates tests but does not specify whether it modifies files, returns code snippets, or requires permissions, leaving 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, with the primary purpose front-loaded and support details in the second sentence. No wasted 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?
For a tool with no output schema and no annotations, the description provides an overview but lacks details on return format, edge cases, and parameter-specific behaviors, leaving gaps for an agent to infer.
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 description coverage is 100%, so parameters are already documented. The description adds value by naming supported frameworks and test types, but this information is largely redundant with the schema's enums.
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 opens with a clear verb-object pair ('Generate comprehensive test cases for code') and specifies supported frameworks and test types, making it distinct from sibling tools like atlas_debug or atlas_refactor.
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 when test generation is needed and lists supported frameworks, but it does not state when not to use this tool vs alternatives, nor does it reference sibling tools.
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?
With no annotations provided, the description carries the full burden. It discloses that the tool 'Creates interactive visualizations' and produces an HTML dashboard, implying a write output and read/analysis of the project. However, it does not mention potential side effects, permissions, or whether it modifies the project, and it lacks details on default behaviors (e.g., which metrics are included by default).
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 concise and front-loaded: two sentences deliver the core purpose and key features without unnecessary fluff or repetition. Every word contributes to conveying the tool's function.
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?
Despite having 11 parameters and no output schema, the description provides a good high-level overview of the tool's capabilities. However, it lacks essential contextual information such as default behavior, prerequisites (e.g., project structure, test data), how the output file is delivered or used, and any constraints of the analysis. Given the tool's complexity, a bit more detail would be valuable.
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 has 100% description coverage for all 11 parameters, each with a clear explanation. The description complements the schema by listing the high-level metric categories (complexity, coverage, etc.) that map to the include* boolean parameters, but it does not add deeper semantics or usage details beyond what the schema already provides. Baseline 3 is appropriate.
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 with a specific verb ('Generate'), a concrete deliverable ('HTML dashboard'), and enumerates the included metrics (complexity trends, test coverage, dependency graphs, security audit, performance hotspots, git stats). This differentiates it from more specialized sibling tools like atlas_security or atlas_git, making it unmistakably an aggregate dashboard generator.
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 for creating a comprehensive dashboard with multiple code metrics, but it does not explicitly state when to use this tool versus the many specialized siblings, nor does it mention exclusions or prerequisites. The context is inferable but not directly demonstrated.
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?
No annotations are provided, so the description carries the full burden. It indicates a read-only operation via 'Analyze' and 'Returns', but it does not disclose potential side effects, error conditions, or how the optional filePath parameter affects results. It does not contradict any annotations (none provided), and the non-destructive nature is reasonably implied.
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 concise sentences with the main purpose front-loaded. Every word earns its place, and it avoids redundant phrasing or unnecessary details.
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 lists high-level returns but does not explain conditional behavior (e.g., whether filePath scopes the results) or the structure of the returned data. Since there is no output schema, more detail about return formats would be helpful, but the current information is adequate for a simple read-only 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 input schema already provides descriptions for all three parameters (100% coverage), so the baseline is 3. The tool description adds no additional semantics about parameter usage, defaults, or how parameters interact with the outputs, so it does not go beyond what the schema already communicates.
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 it analyzes Git history and repository state, with specific outputs (recent commits, active branches, file change history). This is specific enough to distinguish it from all sibling tools, none of which target Git.
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?
While the description implies usage for Git analysis, it provides no explicit guidance on when to use this tool versus alternatives, nor does it mention any limitations or prerequisites. Since there are no similar sibling tools, the context is clear, but the guidance remains implicit rather than explicit.
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?
No annotations are provided, so the description carries the full burden. It discloses several behaviors (analyzes, identifies, detects, generates) but does not state whether the tool modifies code, requires execution, has side effects, or what the output format looks like beyond 'optimization suggestions with benchmark code'. This leaves transparency gaps for a complex profiling 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 two sentences long, front-loaded with the core purpose, and every word adds value. It avoids redundancy or filler, making it highly concise while covering the main capabilities.
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 has 11 parameters and no output schema, the description provides a solid high-level overview but lacks details on the return format, how the benchmarks are presented, and how parameters like expectedInputSize or constraints affect results. It is adequate but incomplete for guiding an agent through a full profiling session.
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 description coverage is 100%, so the structured schema already explains all 11 parameters. The description adds context about the overall goal but does not add insights about any specific parameter beyond what the schema provides. Baseline 3 is appropriate because the schema does the heavy lifting.
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: 'Performance profiling and Big-O analysis' and enumerates specific actions (analyzes time/space complexity, identifies hotspots, detects leaks, generates suggestions). This is a specific resource-focused description that distinguishes it from many sibling tools like atlas_debug or atlas_execution_sandbox by emphasizing complexity analysis and leak detection.
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 when to use the tool (when you need performance profiling or complexity analysis) but does not provide explicit when-to-use versus alternatives, exclusions, or prerequisites. It lacks guidance such as 'for debugging use atlas_debug instead' or 'only for standalone code snippets'.
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. It lists what the tool analyzes, but does not disclose whether it performs read-only operations, requires network access for security/vulnerability checks, generates reports, or has any side effects on the project. The absence of such behavioral context is a significant gap for an analysis 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 entire description is a single sentence that front-loads the core purpose and uses a comma-separated list to enumerate features. Every listed item adds value and there is no redundancy or filler. It is compact and scannable, making it easy for an agent to parse quickly.
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 six parameters and no output schema, the description provides a solid high-level overview of all the analysis dimensions. However, it does not describe the return format, any required prerequisites (e.g., package manager, lock file), or how results are presented. Given the complexity, it's adequately complete but leaves room for more detail on output and usage 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?
Although the input schema provides descriptions for all parameters (100% coverage), the description adds meaning by grouping the parameters into a coherent list of analysis categories (e.g., findUnused -> unused packages, checkSecurity -> security vulnerabilities). It also implies additional analyses like 'dependency graph' and 'circular dependencies' that are not directly represented as parameters, giving richer context.
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 specifies the tool's function: 'Comprehensive dependency analysis' with a list of seven specific analysis types. This distinguishes it from sibling tools like atlas_security or atlas_performance_doctor by covering multiple dependency-related concerns in one place. The verb 'analysis' and resource 'dependencies' make the purpose unambiguous.
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 for a broad dependency health check by listing all the dimensions it covers, but it does not explicitly state when to use this tool versus alternatives like atlas_security for vulnerabilities or atlas_performance_doctor for bundle size. No exclusions or alternative tools are mentioned, leaving the selection to inference.
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?
With no annotations provided, the description carries the full burden. It states the tool analyzes and provides specific code fixes with estimated improvement percentages, which is useful behavioral context. However, it does not disclose whether the tool is read-only, whether it modifies code, or any limitations/side effects, leaving some gaps.
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, front-loaded sentence that efficiently captures the tool's purpose and key capabilities without unnecessary fluff. Every part adds value, and it is easy to parse.
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 summarizes the tool's output (specific code fixes with improvement percentages), which is important since there is no output schema. It provides enough context for a straightforward analyzer, though it could go deeper into return structure or edge cases. Overall, it is reasonably complete given the schema coverage.
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 already documents all parameters with descriptions (100% coverage), so the description does not need to explain parameter details. The description's reference to React/Vue and issue types adds some context but does not go beyond what the schema already provides, so a baseline score of 3 is appropriate.
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 is a frontend performance analyzer with a specific verb 'detects' and lists concrete issue types (re-render issues, bundle bloat, memory leaks). It also mentions providing code fixes with improvement percentages, giving a clear sense of the tool's scope and output.
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 the tool is for frontend performance analysis, which is enough to suggest when to use it, but it does not explicitly state when to prefer it over sibling tools like atlas_profiler or atlas_optimize, nor does it mention exclusions or alternatives.
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 what the tool checks but does not mention whether it is read-only, if it makes network calls, or what the output format is. This lack of context leaves the agent guessing about side effects and trustworthiness.
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, front-loaded sentence that states the core purpose clearly without any filler or redundant information. It earns its place entirely.
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 simple, parameterless introspection tool, the description adequately covers the functionality. It could be enhanced by explicitly noting that it returns a list of providers with their statuses, but given the lack of output schema and low complexity, the current description is almost 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 zero parameters, so there is nothing for the description to explain. The baseline for 0-param tools is 4, and the description appropriately avoids adding irrelevant parameter details.
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 uses a specific verb 'Check' with a clear resource ('available LLM providers') and scope ('their status'). It uniquely identifies the tool's function among the large sibling list, as no other tool appears to deal with provider availability.
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 its use case (checking provider availability/status) but provides no explicit guidance on when to use it versus alternatives. There are no sibling tools with similar functions, so the lack of exclusions is acceptable, but the absence of explicit when-to-use instructions keeps it at a basic level.
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?
With no annotations provided, the description carries the transparency burden. It discloses that it's AI-powered, can detect semantic conflicts, preserve both sides, generate tests, and provide confidence scoring. However, it does not explicitly state whether it modifies files, whether autoResolve causes irreversible changes, or require a clean working tree. The marketing tagline ('Never waste hours') adds no operational transparency. Could be more explicit about safety/state changes.
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 structured as a feature list with a clear title and bullet points. It is reasonably concise and front-loaded. However, the final marketing sentence ('Never waste hours...') is unnecessary and dilutes usefulness. The core bullet list is informative without being bloated, earning a 4.
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?
For a complex tool with 6 parameters and no output schema or annotations, the description covers key features but misses operational details: return values, error conditions, prerequisite git state, and whether auto-resolutions are applied to the working tree. It explains what the tool does but not fully how results are returned or what side effects occur. The schema covers parameters, but the description is not fully complete for 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?
Schema description coverage is 100%, so the baseline is 3. The description mentions features like 'Generate test cases' and 'Confidence scoring', which loosely map to generateTests and confidenceThreshold, but it doesn't add parameter-level semantics beyond the schema. The schema already documents each parameter clearly, so the description adds little extra value.
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: 'Automatically resolve merge conflicts using AI.' It uses a specific verb ('resolve') and resource ('merge conflicts'), and distinguishes itself from sibling tools like atlas_git by focusing on AI-powered conflict resolution, semantic understanding, and test generation. The title also reinforces this.
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 indicates the tool is for merge conflicts and mentions 'Integration with git workflow,' giving clear context for when to use it. It doesn't explicitly state when not to use it or name alternatives, but the focused scope ('merge conflicts') and features like 'autoResolve' and 'interactive suggestions' imply appropriate usage. Lacks explicit exclusions but is fairly clear.
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 provided, the description carries the full burden of behavioral disclosure. It reveals key behaviors: scouring the internet for inspiration, generating code, providing multiple design options, including images, and offering accessibility guidance. This goes beyond a simple purpose statement, though it omits potential limitations or 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a concise two sentences with the core purpose front-loaded. It efficiently lists key features without redundant filler, though words like 'Advanced' and 'best' add slight marketing tone without substantive value.
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 complexity (7 parameters, no output schema, no annotations), the description provides a partial picture. It mentions outputs (images, code, accessibility guidance) but lacks details on the structure of the response, usage prerequisites, or example scenarios. The absence of an output schema makes this gap more significant.
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 has 100% coverage with descriptions for all seven parameters, so the description does not need to explain them. The tool-level description adds context about design options and accessibility but not specific parameter semantics beyond the schema. Baseline 3 is appropriate.
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 states the tool is an 'Advanced UI/UX designer' that performs specific actions: finding design inspirations, generating production-ready code, providing multiple design options, and offering accessibility guidance. It distinguishes from sibling tools like atlas_css_wizard or atlas_animation_studio by focusing on the full UI/UX design process.
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 implicitly conveys that this tool is for UI/UX design tasks, but it does not explicitly state when to use it over alternatives or provide exclusions. There is no mention of when not to use it or how it compares to related tools such as atlas_css_wizard.
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?
With no annotations, the description is the primary source of behavioral info. It mentions what the tool checks (syntax, logic, security, edge cases) and that it provides confidence scoring, which is useful. But it does not disclose the output format, whether the tool modifies the input, or any limitations, leaving ambiguity about expected results.
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, well-structured sentence that front-loads the main action, lists specific validation dimensions, and includes the confidence scoring behavior. Every element earns its place, with no redundancy or filler.
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 covers the core purpose and main checks but falls short on return value expectations and potential side effects. Since there is no output schema, the agent needs more detail on what the tool returns (e.g., confidence score structure, issue list) to fully use it effectively.
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 already provides 100% coverage with clear descriptions for all four parameters, so the description adds minimal semantics. It does implicitly align 'checkTypes' with the listed validation categories, but does not elaborate on 'context' or 'language' 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 function with a specific verb ('Validates') and resource ('code, suggestions, or solutions'), and enumerates the dimensions of validation (syntax, logic, security, edge cases). This distinguishes it from sibling tools like atlas_test or atlas_critique, which have different focus areas.
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 phrase 'before presenting them' provides a clear context for when to use the tool, implying it should be invoked prior to sharing results. However, it does not explicitly mention alternatives or state when not to use it, so exclusion guidance is absent.
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?
Since no annotations are provided, the description carries the burden of behavioral disclosure. It describes the tool's analytical actions (parsing, identifying, suggesting) which imply it is read-only and non-mutating, but it does not explicitly state side effects, permissions, or limitations.
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 concise sentences: the first states the primary purpose, and the second enumerates specific debugging features. Every phrase adds value and there is no redundant filler.
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 optional parameters and no output schema, the description adequately covers the tool's main role and capabilities. It does not describe return-value format, but that is not required given the absence of an output schema, and the feature list gives enough overall 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 input schema already documents all six parameters with descriptions, achieving 100% schema coverage. The tool description adds no new parameter-specific guidance beyond mentioning stack traces and errors, so it meets the baseline without enhancing semantics.
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 opens with a specific verb and resource: 'Analyze errors and provide debugging assistance.' It then details concrete capabilities (parses stack traces, identifies root causes, suggests fixes with code examples, explains common error patterns), which clearly distinguishes it from sibling tools like atlas_test or atlas_error_predictor.
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 makes it evident when to use the tool: whenever a user needs to analyze an error or debug an issue. However, it does not explicitly mention when not to use it or name alternative tools, so it falls short of full exclusions.
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 of behavioral disclosure. It describes the output behavior (findings with severity, CWE IDs, remediation suggestions) and the scope of detection, which provides helpful context. It does not mention external side effects, but 'scan' implies read-only analysis, making this a reasonably transparent description.
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, front-loaded with the primary action ('Scan code for security vulnerabilities'), and contains no redundant or filler language. Every clause adds value—detection types, output fields, and remediation suggestions.
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 tool is fairly simple with three fully described parameters and no output schema. The description compensates by specifying the return content (severity, CWE IDs, remediation) and detection categories. It could be more complete by enumerating supported languages or performance limits, but for a static scanner, it provides sufficient operational 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 input schema already covers all three parameters with descriptions, including 'code' as the code to scan, 'context' as additional context, and 'language' as the programming language. The tool description adds no extra parameter-specific semantics beyond the schema, so the baseline score of 3 is appropriate.
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 scans code for security vulnerabilities, which is a specific verb+resource. It lists concrete vulnerability types it detects (SQL injection, XSS, hardcoded secrets, weak crypto) and what it returns (severity, CWE IDs, remediation), making it distinct from sibling tools like atlas_review or atlas_bug_oracle.
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 when a security scan is needed by stating 'Scan code for security vulnerabilities' and listing detection targets. However, it does not explicitly state when to use this tool over alternatives or provide exclusions, leaving the agent to infer appropriate context from the security-specific language.
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 provided, the description carries the burden of explaining behavior. It discloses capabilities such as 'Each thought can: question previous steps, branch into alternatives, mark dead ends, propose/verify hypotheses, and merge conclusions.' It also mentions dynamic adaptability (confidence tracking, revisions). It does not detail system-level effects (e.g., persistence), but for a cognitive tool, the stated behaviors are substantial and relevant.
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 well-structured and front-loaded with the core purpose. It uses a concise introductory sentence, a clear bulleted list of use cases, and a concluding sentence on capabilities. Every sentence adds value without redundancy. It is appropriately sized for a tool with 18 parameters and complex behavior.
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 tool's complexity (18 parameters) and lack of output schema, the description provides strong context about when and how to use it. It covers the reasoning process, branching, and verification. It does not explain return values or expected output, but the absence of an output schema lowers the burden. The description is sufficiently complete for an agent to decide invocation and understand the tool's capabilities.
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 description coverage is 100%, so the input schema already fully documents all 18 parameters. The description adds no specific parameter-level detail, but it does frame the conceptual purpose of some fields (e.g., branching, hypothesis). This meets the baseline of 3 without exceeding it.
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 function: 'Advanced sequential thinking for complex problem-solving,' with a specific list of features (branching, hypothesis testing, confidence tracking). It goes beyond a mere restatement, though it lacks an explicit verb like 'analyze' or 'reason.' It is distinct from siblings like atlas_decompose or atlas_critique by focusing on iterative thought progression, but 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit usage scenarios with a bulleted list: 'Use this tool for: Breaking down complex problems step-by-step, Multi-path reasoning...' This gives clear context. However, it does not include when-not-to-use guidance or name alternative tools, so it falls short of a full 5.
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?
No annotations are provided, so the description carries the burden. It discloses that the tool returns approaches with pros/cons, which is useful. However, it does not mention side effects, resource implications, or failure modes, leaving a partial transparency gap.
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 with no filler, front-loading the core action ('Generate multiple solution variants') and adding a meaningful detail about the output. It is minimal yet complete.
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 relatively simple generation tool with fully described inputs and no output schema, the description sufficiently covers the core behavior and output type. It doesn't provide extensive examples or edge cases, but given the low complexity, it is adequate. However, it could benefit from a note on how the results should be used in conjunction with other atlas tools.
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?
All four parameters are already described in the schema (100% coverage), so the baseline of 3 applies. The description adds no additional parameter-specific meaning beyond what schema descriptions provide.
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 a specific action (generate multiple solution variants) and a target (coding task), distinguishing it from sibling tools that focus on testing, refactoring, or optimization. The mention of pros/cons further differentiates the output type.
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 implies usage when multiple approaches need to be explored, but does not explicitly state when not to use it or name alternative tools. It provides clear context but lacks exclusions.
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 provided, the description carries the full burden of behavioral disclosure. It mentions 'safely executes,' 'isolated environment,' and 'timeout and resource limits,' which are important behavioral traits. It does not detail output format or error handling, but the core safety and constraint information is present.
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, front-loading the primary purpose and then adding language and constraint details. Every word earns its place, with no fluff or redundancy.
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 tool has five parameters, two required, and no output schema. The description gives a good overview of safety and scope but does not explain behavior such as return values, error scenarios, or how the 'input' and 'expectation' parameters are used. It is adequate but leaves meaningful gaps for an execution 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?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds context about supported languages and resource limits, which complements the schema but does not provide additional semantic meaning. This meets the baseline for high coverage without adding significant value.
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 executes code snippets safely in an isolated environment, with the purpose of testing before suggesting. This is a specific verb+resource combination that distinguishes it from siblings focused on analysis, refactoring, or explanation. The mention of language support further clarifies scope.
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 phrase 'to test before suggesting' provides clear context for when to use the tool—when you need to safely try out code before presenting it. However, it does not explicitly mention alternatives or when not to use it, which is a minor gap given the many sibling tools.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It lists the types of analysis provided but does not explicitly state that the tool is read-only, what the return format looks like, or any limitations (e.g., handling malformed code). The read-only nature is implied by the word 'explain' but not explicitly disclosed.
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, focused sentence. It front-loads the primary purpose ('Explain code in plain language') and then lists key features without unnecessary words. It is concise and well-structured.
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 tool's complexity (7 parameters, no output schema, no annotations), the description does a good job of establishing purpose and core capabilities. However, it lacks explicit details on return value format and how parameters interact (e.g., whether 'includePatterns' is ignored when type is 'line-by-line'). This leaves some gaps for an AI agent, but it is still usable for typical scenarios.
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 descriptions cover all 7 parameters (100% coverage), so the baseline is 3. The description adds value beyond the schema by naming specific explanation modes (line-by-line, algorithm analysis, complexity, design patterns) that map to the 'type', 'includePatterns', and 'includeComplexity' parameters, helping the agent choose appropriate values. It also hints at the 'level' parameter with 'beginner-friendly glossary'.
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 ('Explain code in plain language') and specifies the resource (code). It enumerates distinct capabilities (line-by-line explanations, algorithm analysis, complexity analysis, design pattern detection, glossary) that differentiate it from sibling tools like atlas_docs or atlas_critique.
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 gives clear context for when to use the tool: when code needs to be explained in plain language. It lists typical use cases (line-by-line, complexity, design patterns). However, it does not explicitly mention when not to use it or direct users to alternative tools, such as atlas_docs for documentation generation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![atlas-mcp-server MCP server](https://glama.ai/mcp/servers/IamNishant51/atlas-mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/IamNishant51/atlas-mcp-server)

Score Badge

Copy to your README.md:

[![atlas-mcp-server MCP server](https://glama.ai/mcp/servers/IamNishant51/atlas-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/IamNishant51/atlas-mcp-server)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/IamNishant51/atlas-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server