Agno Docs MCP Server

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct documentation domain (runtime APIs, SDK concepts, code examples, integrations, migration, class reference, and API endpoints). Descriptions include explicit guidance on when to use which tool, minimizing confusion.
Naming Consistency5/5
All tool names follow the 'agno_' prefix plus a clear, descriptive topic in snake_case (e.g., agno_docs, agno_api, agno_reference). The pattern is uniform and predictable.
Tool Count5/5
With 7 tools, the server covers the full scope of documentation needs for the Agno ecosystem without being overwhelming or sparse. Each tool serves a well-defined purpose.
Completeness4/5
The server covers most major documentation areas: SDK concepts, examples, reference, API endpoints, integrations, migration, and runtime. Minor gaps like advanced troubleshooting beyond migration could exist, but the coverage is comprehensive for typical use cases.
Average 4.5/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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

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 accurately states the tool fetches guides and FAQ, implying a read operation, but it does not explicitly state that it is read-only, safe, or free of 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 front-loaded with the primary purpose and usage. The Args list is clear but somewhat long; could be condensed without losing information.
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 covers purpose, parameters, and usage guidance. Given the presence of an output schema (not shown), explanation of return values is unnecessary. The tool is a simple documentation lookup, and the description adequately addresses its context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds significant value by listing concrete topic values ('v2-migration, workflows-migration, ...') and explaining the default behavior ('Leave empty to list all available topics'). This far exceeds the schema's minimal definition.
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 'Get migration guides and FAQ documentation.' It lists specific topics which distinguishes it from sibling documentation tools (agno_docs, agno_reference), but could be more explicit about how it differs from those.
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 explicitly states 'Use for upgrading Agno versions, installation issues, and common errors.' This gives clear context for when to invoke this tool, though it does not list alternatives or when not to use 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?
No annotations are provided, so the description carries full burden. It only describes basic functionality and valid parameter values, but lacks disclosure of authentication needs, rate limits, error handling, or potential side effects. This is minimal for a read 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: a brief purpose statement, followed by well-structured Args and Examples sections. Every sentence adds value with no redundancy.
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 covers the input parameters sufficiently with valid values and examples. However, it does not describe the output format or what the returned documentation looks like, though an output schema is present (not shown). Slightly incomplete without output details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description adds significant meaning: it explains the integration_type enum, provides specific name values, and gives examples. This fully compensates for the missing schema descriptions.
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 'gets integration documentation for databases, vector stores, and models,' specifying the verb and resource. It lists integration types and examples, distinguishing it from sibling tools like agno_agentos or agno_api.
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 guidance on parameter values (e.g., integration_type options, name options) and examples. However, it does not explicitly state when to use this tool over siblings or when not to use it, but the context is self-explanatory.
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 explains what the tool retrieves (documentation) but does not disclose behavioral traits such as whether it makes network calls, caching behavior, response format, or whether it is real-time. A 3 is appropriate because it covers basic purpose but lacks deeper behavioral context.
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 with bullet points for sections and usage guidelines. It is front-loaded with the main purpose and uses concise sentences. Every sentence serves a clear function without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple documentation retrieval tool with one optional parameter, the description covers all essential aspects: purpose, usage context, parameter guidance, and exclusion criteria. Despite having an output schema not detailed, the description is sufficient for an agent to correctly select and invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the schema: it explains the 'path' parameter with practical examples like 'api/', 'features/memories', etc., and advises 'Leave empty for overview.' This compensates for the 0% schema description coverage, making the parameter highly actionable.
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 'Get AgentOS runtime and deployment documentation' and lists specific sections, distinguishing it from sibling tools like agno_docs and agno_reference. The verb 'Get' and resource 'AgentOS runtime and deployment documentation' make the 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.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'USE THIS TOOL for questions about...' and provides a list of use cases, followed by 'For SDK code usage, use agno_docs or agno_reference instead.' This gives clear when-to-use and when-not-to-use guidance with named alternatives.
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 full burden. It states the tool returns complete, runnable Python examples and clarifies these are SDK examples for writing agent code, not deployment. This is sufficient for a read-only retrieval tool, though it omits potential side effects or auth requirements.
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 with an Args section and a clear return statement. Every sentence is informative and earns its place; no wasted words.
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 simplicity (one optional parameter, outputs examples) and presence of an output schema, the description covers the essentials. It explains what the tool returns and its scope, though it could mention error handling or pagination, but that is not critical for this use case.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description fully explains the single parameter 'category', listing all valid values and the effect of leaving it empty. This adds critical meaning beyond the bare schema definition.
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?
Clearly states it gets SDK code examples for building agents with Agno. The description specifies the return value (complete, runnable Python code) and distinguishes from sibling tool agno_agentos for deployment examples. No ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly tells when to use this tool ('for SDK examples') and when to use the alternative ('agno_agentos for deployment and hosting'). Also instructs to leave category empty to list all categories, providing clear usage guidance.
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 explains it is a read-only reference tool that returns documentation. It does not specify side effects, but that is appropriate. Provides good behavioral context.
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?
Short, front-loaded description with no wasted words. Uses a clear structure: purpose, args, and differentiation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter and no annotations, the description covers purpose, parameter semantics, and usage context completely. Output schema exists but is not needed for completeness.
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?
Despite 0% schema coverage, the description adds a clear list of allowed topics and explains that the topic selects the SDK class documentation. This compensates well.
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 states it gets Agno SDK class and method reference, with specific examples of what it returns (Agent() constructor parameters, etc.). It distinguishes from sibling agno_agentos by noting the latter is for runtime REST API endpoints.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly mentions when to use this tool for SDK docs and when to use agno_agentos for runtime API endpoints. Also lists allowed values for the topic parameter.
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?
No annotations provided, so description carries full burden. It explains return format (method, path, parameters, body, response codes) and input options. Does not mention side effects, but as a read-only documentation tool, no further disclosure needed.
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?
Well-structured with Args, Returns, and usage guidance sections. Every sentence adds value; no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one parameter fully documented and return values explained, description is complete. Sibling differentiation and output schema mention ensure no gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but description adds significant meaning: lists valid resource values and explains default behavior. This compensates fully for the missing schema descriptions.
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 it gets AgentOS REST API endpoints from OpenAPI spec, with specific verb and resource. Differentiates from siblings by referencing agno_reference for SDK and agno_agentos for conceptual docs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use (REST API queries) and when not (SDK or conceptual), providing alternative tools. Also specifies that leaving resource empty lists all resources.
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?
No annotations are provided, so the description carries the burden of behavioral disclosure. It implies a safe, read-only operation by describing the tool as getting documentation. It does not explicitly mention non-destructiveness, but the nature of a doc retrieval tool makes it obvious. Could mention that no side effects occur.
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: it starts with a clear one-sentence purpose, then breaks into an Args section with a list of example paths. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists (not shown), the description does not need to explain return values. It covers what the tool does, how to use the parameter, when to use this tool vs. alternatives, and provides a complete set of example paths. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'path' has 0% schema coverage, meaning the schema provides no description. The full description compensates by listing example paths (e.g., 'basics/agents/', 'basics/tools/') and explaining the path structure, adding meaning 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 retrieves Agno SDK documentation for writing agent code. It distinguishes itself from the sibling tool 'agno_agentos' by specifying that tool covers deployed agent REST APIs and runtime features, while this tool is for SDK/library usage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided: 'This is for SDK/library usage... For deployed agent REST APIs and runtime features, use agno_agentos instead.' Additionally, it suggests using 'basics/' to see all topics and provides a list of example paths for common use cases.
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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