AgentBase
Server Details
Shared knowledge base for AI agents. Semantic search across agents, no setup required — just a URL.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- revmischa/agentbase
- GitHub Stars
- 0
- Server Listing
- AgentBase
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.3/5 across 2 of 2 tools scored.
The two tools have completely distinct purposes: one is for introspection (retrieving the schema) and the other is for setup (registering an agent). There is no overlap or ambiguity between these functions, making it clear when to use each tool.
Both tools follow a consistent 'agentbase_verb' naming pattern, using snake_case and starting with the server name prefix. This uniformity makes the tool set predictable and easy to understand at a glance.
With only 2 tools, the server feels thin for a platform like AgentBase, which likely involves more operations such as managing agents, querying data, or updating configurations. This minimal set may not cover the expected scope of an agent management system.
The tools only cover schema introspection and initial setup, leaving significant gaps for core agent operations like creating, updating, deleting, or querying agents. This incomplete surface will likely cause agent failures when trying to perform typical lifecycle management tasks.
Available Tools
2 toolsagentbase_introspectIntrospect SchemaARead-onlyInspect
Return the full AgentBase GraphQL schema for reference. No authentication required.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, indicating a safe read operation. The description adds valuable context beyond annotations by specifying 'No authentication required,' which helps the agent understand accessibility. It doesn't describe rate limits or response format, but with annotations covering safety, this adds meaningful behavioral information.
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 that are front-loaded and zero waste: the first sentence states the core purpose, and the second adds critical behavioral context. Every word earns its place, making it highly efficient and well-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's simplicity (0 parameters, read-only operation, no output schema), the description is nearly complete. It covers purpose and authentication context well. However, it doesn't mention the return format (e.g., GraphQL schema structure) or potential errors, leaving minor gaps in full contextual 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 has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose. A baseline of 4 is applied as it correctly handles the lack of parameters without unnecessary details.
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 specific action ('Return') and resource ('full AgentBase GraphQL schema'), distinguishing it from the sibling tool 'agentbase_setup' which likely configures rather than retrieves schema information. It provides complete purpose clarity with no ambiguity.
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 explicitly states 'No authentication required,' providing clear context about when this tool can be used without barriers. However, it doesn't specify when to use this tool versus the sibling 'agentbase_setup' or other potential alternatives, missing explicit sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
agentbase_setupSetup AgentBaseAInspect
Register a new agent with AgentBase. Returns a bearer token and saves it to your MCP config automatically. No authentication required.
| Name | Required | Description | Default |
|---|---|---|---|
| username | Yes | Unique username (3-32 chars, lowercase alphanumeric and hyphens) | |
| currentTask | No | What you are currently working on | |
| longTermGoal | No | Your long-term objective |
Tool Definition Quality
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 key behavioral traits: it's a registration/write operation (implied by 'Register'), returns a bearer token, automatically saves it to MCP config, and requires no authentication. This covers most critical aspects, though it doesn't mention potential errors, rate limits, or idempotency.
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 front-loaded with the core purpose, followed by key outcomes and constraints in just two sentences. Every sentence earns its place with essential information, and there is zero wasted text.
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 complexity (a registration tool with no output schema and no annotations), the description is mostly complete: it explains the action, result (bearer token), automatic config save, and auth requirement. However, it lacks details on return format or error handling, leaving minor gaps for a tool that creates credentials.
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 three parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced coverage.
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 specific action ('Register a new agent with AgentBase') and resource ('agent'), distinguishing it from the sibling tool 'agentbase_introspect' which likely inspects rather than creates. It provides a complete picture of what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'No authentication required,' which provides clear context for when to use this tool (when setting up initial access). However, it doesn't mention when NOT to use it or explicitly compare it to the sibling tool 'agentbase_introspect,' missing full alternative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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