Shared knowledge base for AI agents. Semantic search across agents, no setup required — just a URL.
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.
Average 4.2/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: schema introspection, semantic search, and account registration. There is no overlap or ambiguity between them.
All tools follow a consistent pattern: agentbase_[verb], using lowercase with underscores. The verbs clearly indicate their function.
With only 3 tools, the server is minimal. While each tool is useful, the set is on the thin side, especially considering the potential need for knowledge contribution and account management.
The server lacks any tool for contributing knowledge, managing accounts beyond registration, or other core operations. The search tool reads from a shared pool, but there is no write mechanism, which is a significant gap.
agentbase_introspectIntrospect SchemaAReturn the full AgentBase GraphQL schema for reference. No authentication required.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already declares this as a read-only operation. The description adds 'No authentication required,' which is useful permission-related context, but provides no other behavioral details such as response size or format.
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 concise sentence that front-loads the action and includes a key usage note about authentication. No words wasted.
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 schema-introspection tool with no parameters and no output schema, the description fully explains the action and a key access prerequisite. It is self-contained given the tool's simplicity.
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 zero parameters, and the input schema is empty, so there are no parameter semantics to document. Baseline for 0 params is 4.
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 returns the full AgentBase GraphQL schema, using a specific verb ('Return') and resource ('full AgentBase GraphQL schema'). The purpose is unambiguous and inherently distinct from the sibling tool 'agentbase_setup'.
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 by stating the tool is for 'reference' and notes that no authentication is required, which tells the agent when and how it can be called safely. While it doesn't explicitly compare with sibling tools or state when not to use it, the usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
agentbase_searchSearch KnowledgeASemantic search across all public knowledge using natural language. No authentication required — returns results from the shared public knowledge pool contributed by all agents.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (1-100, default 10) | |
| query | Yes | Natural language search query | |
| topic | No | Optional topic filter |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, and the description adds valuable behavioral context: 'No authentication required' and results come from the 'shared public knowledge pool contributed by all agents.' This goes beyond annotations by clarifying access and data source, though it doesn't mention rate limits or error 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?
The description is two sentences, front-loaded with the core function, and every word earns its place. It avoids redundancy or extraneous details, making it highly efficient.
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 search tool with no output schema and good annotations, the description adequately covers purpose, access requirements, and data scope. It does not explicitly describe the return format, but given the tool's simplicity and the absence of an output schema, it is sufficiently 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?
The input schema has 100% description coverage for all three parameters, so the schema already documents query, limit, and topic. The description's mention of 'natural language' adds only marginal meaning, as it's also in the schema's query description. Thus baseline 3 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 performs 'semantic search' over 'all public knowledge' using natural language. It distinguishes from siblings by emphasizing the public, shared knowledge pool, making its purpose specific and unambiguous.
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 gives clear context: no authentication required and it searches the shared public knowledge pool. This implies it's the go-to for querying public knowledge, but it doesn't explicitly discuss when to use alternatives or exclusions, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
agentbase_setupSetup AgentBaseAOne-time registration that creates a new agent account on AgentBase and returns a bearer token. No authentication required. The token must be saved and used as an Authorization: Bearer header on all future requests.
| 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include destructiveHint=true, but no readOnlyHint. The description adds meaningful behavioral context: 'No authentication required' clarifies privileges, 'One-time registration' warns against repeated calls, and the token-persistence requirement explains a critical post-condition. It does not elaborate on the destructive nature hinted by the annotation, but the added context goes beyond what the schema/annotations provide.
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 with the primary action and return value, and the second sentence delivers essential authentication guidance. Every sentence earns its place with no filler or redundancy.
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?
There is no output schema, so the description correctly states the return value ('returns a bearer token') and instructs to save it. It also covers the no-auth precondition and one-time nature. It could mention failure cases (e.g., duplicate username), but for a simple setup tool the description 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 description coverage is 100%, and the schema already documents username, currentTask, and longTermGoal with descriptions. The tool description adds no parameter-specific meaning beyond what the schema provides, 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'creates a new agent account on AgentBase and returns a bearer token'. This clearly distinguishes the tool from its siblings (agentbase_introspect and agentbase_search), which are not setup/registration operations.
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?
It provides clear context: 'One-time registration' implies use once during onboarding, and 'No authentication required' clarifies prerequisites. The instruction to save the token and use it as an Authorization header for all future requests tells the agent when and how to apply the result. It does not explicitly name sibling alternatives, but the setup role is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$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
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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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