AgentNave
Server Quality Checklist
Latest release: v0.2.0
- Disambiguation5/5
Each tool targets a distinct lifecycle phase: starting, observing, or stopping an invocation. wait_agent and cancel_agent are clearly differentiated by whether the caller wants to continue observing or actively terminate the work.
Naming Consistency5/5All three names follow the same verb_agent convention with clear verbs: start, wait, cancel. There are no mixed naming styles or vague identifiers.
Tool Count5/5Three tools is a well-scoped count for a focused agent lifecycle server. Each tool fulfills a necessary operation without redundancy or bloat.
Completeness5/5The lifecycle is complete: start an agent, wait for its progress or final result, and cancel when needed. wait_agent also returns final results, so there is no dead-end after an invocation completes.
Average 4.5/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto 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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructive and idempotent behavior. The description adds useful nuance by explaining that the tool returns the final cancelled or already-terminal result, meaning it safely handles cases where the invocation is already done. This complements the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences carry all necessary information, with the core action front-loaded and the alternative tool guidance placed second. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, one-parameter tool with an output schema and informative annotations, the description is fully sufficient. It explains what the tool does, when to use it, and how it differs from the sibling tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the invocation_id parameter is well described as the ID returned by start_agent. The description adds minimal extra meaning beyond the schema, but the single-parameter context is straightforward.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear, specific action: stop one invocation and return its cancelled or already-terminal result. It names the resource (agent invocation) and the effect, and distinguishes itself from wait_agent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided: use this tool only when the Manager intends to stop active provider work, and use wait_agent to observe without stopping. This directly tells an agent when to choose this tool versus its sibling.
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?
Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds behavioral context beyond those: returning a running snapshot means the invocation remains active and further calls to wait_agent are expected. This goes beyond simple annotation repetition.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact: three sentences that front-load the core function, then provide the lifecycle note and the sibling alternative. Every sentence earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is an output schema and rich annotations, the description covers the essential lifecycle behavior (call again if running), references the sibling for cancellation, and is sufficient for correct invocation. Nothing important is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 does not add parameter-specific meaning beyond the schema—the schema already explains invocation_id and wait_timeout_seconds. The word 'briefly' loosely hints at timeout but adds little.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Wait briefly for one invocation') and the two possible outcomes ('running snapshot or its final result'). It differentiates from siblings by explicitly addressing the running case and referencing cancel_agent as the stopping mechanism.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit usage guidance is given: 'A running response leaves the invocation active; call wait_agent again later. Use cancel_agent only when the invocation should be stopped.' This tells the agent exactly when to reuse this tool and when to switch to a sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states that the launched provider may read, write, or run commands in cwd subject to native permission controls, which meaningfully expands on the annotations' destructiveHint and openWorldHint. It also discloses the non-blocking behavior, helping the agent understand the tool's side effects before invoking it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded: the first sentence establishes the essential behavior, and the second sentence provides the key follow-up action and side-effect warning. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the critical non-blocking launch behavior, required follow-up with wait_agent, and potential destructive effects. An output schema exists, so the return value does not need to be described. Minor gaps like when to use cancel_agent remain, but they are not essential for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and each parameter already carries a meaningful description. The tool description adds no new parameter-level semantics, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('Start'), the resource ('one subagent'), and the key outcome ('return its in-memory invocation ID without waiting'). This sharply distinguishes start_agent from its wait_agent and cancel_agent siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells the agent to use wait_agent with the returned ID to observe the invocation, which is the natural alternative workflow. This is direct, actionable guidance for choosing and sequencing this tool.
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:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/TimWongUp/agentnave'
If you have feedback or need assistance with the MCP directory API, please join our Discord server