CCG MCP Tool is a unified MCP server that integrates Claude Code, OpenAI Codex, and Google Gemini into a single workflow for AI-powered code analysis, ideation, review, and multi-agent orchestration.
ask-ai: Send prompts to Gemini, Codex, or Claude for code analysis, file inspection (via @ syntax), and structured edit suggestions; supports sandbox mode (Gemini) and chunked responses for large outputs.
brainstorm: Generate ideas using creative frameworks (SCAMPER, Design Thinking, lateral thinking, etc.) with feasibility analysis, domain context integration, and configurable idea counts.
mitigate-mistakes: Apply targeted, research-grounded skill gates (e.g., requirements-grounding, secure-coding, dependency-verification) to detect common AI coding mistakes at specific development stages.
coordinate-review: Run an automated multi-gate code review tailored to a task type (feature, bugfix, refactor, dependency-update), producing gate-by-gate findings and a merge recommendation.
deploy-agents: Orchestrate up to 10 AI agents in parallel, sequential, or fan-out strategies for collaborative or independent task execution, with support for Claude Code Agent Teams.
agent-status: Monitor and inspect active or recent multi-agent orchestration sessions.
fetch-chunk: Retrieve paginated chunks from large cached responses (e.g., structured change mode outputs).
ping: Test connectivity to the MCP server.
Help: Display help information for the configured AI provider CLI.
timeout-test: Developer utility to test timeout prevention behavior.
Allows analyzing codebases using Google Gemini's massive context window within a unified workflow.
Provides precision code edits and analysis through integration with OpenAI Codex.
MCP server that integrates Claude Code, OpenAI Codex, and Google Gemini into a single workflow. Analyze codebases with Gemini's massive context window, get precision edits with Codex, or use Claude's reasoning — all from one MCP interface.
Related MCP server: MCP Coding Agents
claude mcp add ccg-tool -- npx -y @diazstg/ccg-mcp-toolOr configure manually:
{
"mcpServers": {
"ccg-tool": {
"command": "npx",
"args": ["-y", "@diazstg/ccg-mcp-tool", "--provider", "gemini", "--agent-mode", "read-only"]
}
}
}Node.js v16+
At least one AI CLI: Gemini CLI, Codex CLI, or Claude Code
Tool | Description |
| Universal AI analysis across providers |
| Structured ideation (SCAMPER, lateral, etc.) |
| Research-grounded gates for common AI failures |
| Coordinated multi-gate review by task type |
| Multi-agent orchestration (parallel/sequential/fan-out) |
| Monitor orchestration sessions |
| Retrieve chunks from large responses |
| Test connectivity |
| List available tools |
| Developer timeout testing |
MIT — see LICENSE.
Disclaimer: This is an unofficial tool and is not affiliated with Google, OpenAI, or Anthropic.
Check status of multi-agent orchestration sessions. Provide a sessionId to get details, or omit to list recent sessions. Current agent mode: read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| sessionId | No | Session ID to query (e.g., 'ses_abc123'). Omit to list recent sessions. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description mentions read-only mode but lacks details on authentication, rate limits, error handling, or what happens with invalid sessionIds.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded, and every word serves a purpose. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple status check tool with one optional parameter, the description covers the key functionality and usage. It could mention return format or pagination but is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the description adds value by explaining the effect of providing vs omitting the sessionId, but does not add 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks status of multi-agent orchestration sessions, with two distinct usage modes (with or without sessionId). It uses specific verb+resource and distinguishes itself from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool (checking session status) and the two modes, but does not explicitly state when not to use or mention alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Provider selection [--provider], model selection [-m], sandbox [-s], and changeMode:boolean for providing edits. Supports Gemini, Codex, and Claude Code.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Analysis request. Use @ syntax to include files (e.g., '@largefile.js explain what this does') or ask general questions | |
| provider | No | Provider to use (e.g., 'gemini', 'codex', 'claude'). Defaults to server config ('gemini'). | gemini |
| model | No | Optional model to use (e.g., 'gemini-2.5-flash', 'gpt-5.3-codex'). If not specified, uses the server default (provider default). | |
| sandbox | No | Use sandbox mode (-s flag, Gemini only) to safely test code changes, execute scripts, or run potentially risky operations in an isolated environment | |
| changeMode | No | Enable structured change mode (Gemini only) - formats prompts to prevent tool errors and returns structured edit suggestions that Claude can apply directly | |
| chunkIndex | No | Which chunk to return (1-based) | |
| chunkCacheKey | No | Optional cache key for continuation |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description should disclose behavioral traits. It does not mention that sandbox and changeMode are Gemini-only (hidden in schema), nor does it discuss authentication, rate limits, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence listing options, which is concise but lacks structure and is not front-loaded with a clear purpose statement.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters, no output schema, and no annotations, the description is incomplete. It does not explain return values, continuation mechanics (chunkIndex, chunkCacheKey), or practical usage scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description briefly restates a few parameters (provider, model, sandbox, changeMode) but adds little beyond the schema descriptions, which are already detailed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description implies the tool is for querying an AI and mentions provider/model selection, sandbox, and changeMode. It distinguishes itself from siblings like 'brainstorm' or 'coordinate-review' by focusing on AI querying, but lacks an explicit verb+resource phrase.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus other tools or when not to use it. It only lists optional flags, 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.
Generate novel ideas with dynamic context gathering. --> Creative frameworks (SCAMPER, Design Thinking, etc.), domain context integration, idea clustering, feasibility analysis, and iterative refinement. Supports Gemini, Codex, and Claude.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Primary brainstorming challenge or question to explore | |
| provider | No | Provider to use (e.g., 'gemini', 'codex', 'claude'). Defaults to server config ('gemini'). | gemini |
| model | No | Optional model to use (e.g., 'gemini-2.5-flash'). If not specified, uses the server default (provider default). | |
| methodology | No | Brainstorming framework: 'divergent' (generate many ideas), 'convergent' (refine existing), 'scamper' (systematic triggers), 'design-thinking' (human-centered), 'lateral' (unexpected connections), 'auto' (AI selects best) | auto |
| domain | No | Domain context for specialized brainstorming (e.g., 'software', 'business', 'creative', 'research', 'product', 'marketing') | |
| constraints | No | Known limitations, requirements, or boundaries (budget, time, technical, legal, etc.) | |
| existingContext | No | Background information, previous attempts, or current state to build upon | |
| ideaCount | No | Target number of ideas to generate (default: 10-15) | |
| includeAnalysis | No | Include feasibility, impact, and implementation analysis for generated ideas |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description should carry the full burden. It mentions 'dynamic context gathering' and features like idea clustering and feasibility analysis, but lacks details on behavioral traits such as rate limits, authorization needs, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with one sentence and a bullet-like list of features. It is front-loaded with the purpose and uses an arrow to separate key highlights. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 9 parameters and no output schema. The description provides an overview of features but does not explain the return format, how results are structured, or how the 'dynamic context gathering' works in practice. More detail would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description does not add significant meaning beyond the parameter descriptions in the schema. For example, the 'methodology' parameter is already well-described in the schema with an enum.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate novel ideas with dynamic context gathering.' It lists creative frameworks and features, making it distinct from siblings like 'ask-ai' or 'deploy-agents'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions supported providers and frameworks but does not provide explicit guidance on when to use this tool versus alternatives. No when-to-use or when-not-to-use context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Coordinated multi-gate review. Auto-selects the minimum set of research-grounded skill gates for a given task type (feature/bugfix/refactor/dependency-update) and runs them in a single structured pass. Produces gate-by-gate findings, blocking issues, and a final merge recommendation. Uses the AI Coding Agent Mitigator coordinator pattern.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The code, diff, or task description to review. | |
| task_type | Yes | The type of change being made. Determines which minimum skill gates are applied: feature (8 gates), bugfix (7 gates), refactor (7 gates), dependency-update (7 gates). | |
| provider | No | Provider to use (e.g., 'gemini', 'codex', 'claude'). Defaults to server config ('gemini'). | gemini |
| model | No | Optional model override. Defaults to server config (provider default). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It clearly explains the tool runs a set of gates, produces findings and blocking issues, and gives a merge recommendation. The mention of the coordinator pattern adds behavioral context. It does not mention side effects, but as a review tool, read-only behavior is implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, each contributing essential information. It front-loads the core purpose and uses no unnecessary words. Every sentence earns its place with specific details about function, task types, and output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multi-gate review, multiple parameters, no output schema), the description adequately explains what it does and what it produces. It covers inputs from the schema and gives a clear idea of outputs, though it could mention potential failure modes or prerequisites for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all four parameters. The description adds limited extra meaning beyond what is in the schema, such as explaining that task_type determines gate count. Baseline 3 is appropriate as the description compensates slightly but not significantly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a coordinated multi-gate review, auto-selecting skill gates based on task type and producing findings and a merge recommendation. This distinguishes it from all sibling tools (e.g., 'mitigate-mistakes', 'ask-ai') by its specific function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context for when to use the tool—specifically for task types feature, bugfix, refactor, or dependency-update. However, it does not explicitly state when not to use it or compare to alternatives, leaving usage decision somewhat implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Deploy multiple AI agents to work on tasks collaboratively or independently. Strategies: 'parallel' (N agents on 1 task), 'sequential' (chained, each sees prior output), 'fan-out' (1 agent per task). Agents communicate via shared context to avoid conflicts and redundancy. Current agent mode: read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| tasks | Yes | Array of task descriptions. Single task = collaborative (parallel/sequential). Multiple tasks = fan-out (one agent per task). | |
| agentCount | No | Number of agents to deploy for parallel/sequential strategies (2-10). Ignored in fan-out mode where agent count = number of tasks. | |
| strategy | No | Execution strategy: 'parallel' = all agents work on 1 task concurrently, 'sequential' = agents chain, each sees prior outputs, 'fan-out' = each agent gets a separate task. | parallel |
| provider | No | CLI provider to use ('claude', 'codex', 'gemini'). Defaults to 'gemini'. | gemini |
| model | No | Optional model override. Defaults to server config. | |
| maxConcurrency | No | Max parallel agent processes (1-10, default 3). Controls resource usage. | |
| context | No | Shared context injected into every agent's prompt. Use for project background, constraints, or coordination instructions. | |
| useAgentTeams | No | Enable Claude Code Agent Teams (Claude provider only). Delegates team coordination to Claude Code's native Agent Teams feature. Requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS enabled in Claude Code settings. See: https://code.claude.com/docs/en/agent-teams |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It mentions 'read-only' mode and agent communication, but does not disclose side effects, resource implications, or post-deployment behavior. Increased transparency would improve agent decision-making.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences efficiently convey purpose, strategies, and a behavioral qualifier, with no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers core functionality and strategies but lacks information on return values and post-deployment actions, leaving some gaps for a complex tool. Considering the absence of output schema, more details on outcomes would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides full descriptions, but the description adds context on strategies and shared context, enhancing understanding beyond the schema. It justifies the 'context' parameter and clarifies strategy semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Deploy' and the resource 'multiple AI agents', and outlines three specific strategies (parallel, sequential, fan-out), making it distinct from sibling tools like agent-status or ask-ai.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for multi-agent deployment but does not explicitly differentiate from sibling tools or state when not to use this tool. The strategy descriptions guide parameter selection but not tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieves cached chunks from a changeMode response. Use this to get subsequent chunks after receiving a partial changeMode response.
| Name | Required | Description | Default |
|---|---|---|---|
| cacheKey | Yes | The cache key provided in the initial changeMode response | |
| chunkIndex | Yes | Which chunk to retrieve (1-based index) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose any behavioral traits beyond fetching. It implies the operation is safe and read-only, but lacks explicit mention of side effects, auth requirements, or cache expiration behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences that are direct and contain no filler. Every word adds value, making it highly concise and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description does not explain the return format or behavior for edge cases (e.g., invalid chunk index, cache expiration). Since there is no output schema, more detail about expected output would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds no additional information beyond what the schema already provides for parameters. Baseline 3 is appropriate as the schema handles parameter documentation fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it retrieves cached chunks from a changeMode response, a specific verb and resource. It also indicates the usage context for getting subsequent chunks, which distinguishes it from sibling tools that are unrelated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this to get subsequent chunks after receiving a partial changeMode response', providing clear guidance on when to use. Does not mention alternatives or when not to use, but the specific context suffices.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Display help information for the configured AI provider CLI
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavioral traits. It only states 'Display help information' without mentioning side effects, safety, or read-only nature. Minimal disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence conveys the purpose with no wasted words. Ideal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema), the description is largely sufficient. It clearly indicates what the tool does, though a brief note on return format would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, and schema coverage is 100%. The description adds no param info, but none is needed. Baseline 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool displays help information for the AI provider CLI, distinguishing it from sibling tools like agent-status or ask-ai. However, it could be more specific about which CLI.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. While help tools are somewhat self-explanatory, explicit context would improve decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Apply a single research-grounded skill gate to detect common AI coding mistakes. Choose the gate relevant to your current task stage: requirements-grounding, context-scope-discipline, dependency-verification, design-doc-and-architecture-gate, test-and-error-path-gate, secure-coding-and-validation-gate, code-review-and-change-gate, code-quality-enforcer, or deterministic-validation-gate. Based on academic research and professional engineering standards.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The code snippet or task you want to check for potential AI coding agent mistakes. | |
| skill | Yes | The mitigation skill gate to apply. One of: requirements-grounding, context-scope-discipline, dependency-verification, design-doc-and-architecture-gate, test-and-error-path-gate, secure-coding-and-validation-gate, code-review-and-change-gate, code-quality-enforcer, deterministic-validation-gate. | |
| provider | No | Provider to use (e.g., 'gemini', 'codex', 'claude'). Defaults to server config ('gemini'). | gemini |
| model | No | Optional model override. Defaults to server config (provider default). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions the tool is research-grounded and detects mistakes, but does not explain side effects, response format, or what happens if no mistakes are found.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the purpose, and lists gates efficiently. It is concise and lacks fluff, though it could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters and no output schema, the description covers the function and skill parameter but omits return value, error handling, or expected output format, leaving gaps for a complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds context that the 'skill' parameter comprises research-grounded gates tied to task stages, but provides no additional detail per parameter beyond the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool applies a skill gate to detect AI coding mistakes, and lists the specific gates. It distinguishes the tool's unique function from siblings like 'ask-ai' or 'brainstorm', but does not explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises choosing the gate relevant to the current task stage, implying when to use it. However, it offers no guidance on when not to use this tool or alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Echo
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | No | Message to echo |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and a one-word description, the tool's behavior is opaque. It does not disclose side effects, authentication needs, return values, or whether it's a read or write operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
While the description is extremely concise, it is under-specified. The single word 'Echo' fails to provide essential structure or front-loaded information, making it unhelpful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one optional parameter and no output schema, the description is critically insufficient. It does not explain what the tool returns or how it behaves, leaving agents without necessary context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although the single parameter 'prompt' has a schema description ('Message to echo'), the tool description adds no further meaning beyond what the schema already provides. The parameter role is left implicit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Echo' is minimal and tautological, merely restating the name 'ping' without clarifying whether it performs a network connectivity test or simply returns the input prompt. It lacks specificity about the verb and resource.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool or how it differs from sibling tools like 'agent-status' or 'timeout-test'. The description offers no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Test timeout prevention by running for a specified duration
| Name | Required | Description | Default |
|---|---|---|---|
| duration | Yes | Duration in milliseconds (minimum 10ms) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It only mentions 'running for a specified duration' but does not clarify whether the tool is safe (read-only), modifies state, or has side effects. The behavior is ambiguous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler, earning high marks for brevity. However, it may be too terse for a tool that could benefit from more context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description fails to explain return values, side effects, or what 'test timeout prevention' means, leaving gaps in understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage with a description for 'duration'. The tool description adds no new parameter information beyond what the schema offers, so baseline score applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action (test timeout prevention) and resource (running for a duration). It distinguishes itself from sibling tools like agent-status or brainstorm, which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. There is no mention of context, prerequisites, or exclusions, leaving the agent without decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.3.1agent-statusask-aibrainstormcoordinate-reviewdeploy-agentsfetch-chunkHelpmitigate-mistakespingtimeout-testMost tools have distinct purposes, but ask-ai and brainstorm overlap in using AI models, and coordinate-review and mitigate-mistakes both involve code review gates. Descriptions help differentiate, but some confusion remains.
Names mix styles: some single-word (brainstorm, ping, Help), some hyphenated verb-noun (ask-ai, coordinate-review, deploy-agents, fetch-chunk, mitigate-mistakes), and 'Help' is capitalized. No consistent pattern.
With 10 tools covering agent status, deployment, AI queries, brainstorming, code review, and utilities, the count is well-scoped for a development assistant server. No unnecessary tools.
The tool set covers core workflows: AI interaction, agent orchestration, code review, and mistake detection. Missing tools for session management or configuration, but the surface is largely complete for its domain.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
Real-time chat hub for AI agents — Claude Code, Cursor, Cline, Codex over MCP or REST.
One MCP endpoint for Claude, GPT & Gemini: 100+ tools + no-code connectors + agent workers.
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