Archangel Agency MCP

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It clearly states the output contents (skills, capabilities, payment methods, contact info) and implies a read-only retrieval operation. It does not mention potential limitations or errors, but for a straightforward getter, it is reasonably transparent.

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, front-loaded sentence that conveys the essential purpose without any unnecessary words. Every element—the verb, the resource, and the specific entity—adds value, making it concise 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?

For a tool with no parameters and no output schema, the description provides a fairly complete overview by listing the categories of information included in the returned agent card. However, it does not specify the response format (e.g., JSON) or include any caveats, so it stops short of full 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 zero parameters, so the description cannot add parameter details. According to the rubric, a baseline of 4 applies when there are no parameters, and the description correctly focuses on what the tool returns rather than missing parameter 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 tool's function: returning the A2A agent card for a specific entity (Jono Tho'ra / Archangel Agency). It enumerates the card's contents (skills, capabilities, payment methods, contact info), making it distinct from sibling tools that likely handle contexts, services, or language briefings.

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 the tool is used to retrieve agent information for the named entity, but it does not explicitly state when to prefer this over sibling tools or provide exclusions. The context is clear enough for a simple getter, but no direct guidance on alternatives is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It reveals that the tool is a read-only retrieval operation ('Returns') and adds context about the document type ('designed for AI consumption'), but it does not explicitly state side effects, error behavior for invalid context names, or confirm there are no mutation risks. This is adequate for a simple getter but leaves some gaps.

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 purpose, and each sentence earns its place. It avoids redundancy and provides just enough detail, making it concise 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 (one optional parameter, no output schema, no annotations), the description combined with the rich schema is complete. It explains what the tool returns, how to use the parameter, and the nature of the documents, fully equipping an agent to invoke it correctly.

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 of the 'context' parameter, including an enum and a description explaining 'list' and specific values. The tool description largely repeats this information, adding only a general phrase about 'structured ontological frameworks.' Since the schema fully defines the parameter semantics, the description adds no significant incremental value, so 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's verb ('Returns') and resource ('directory of Context JSON briefing documents'), and even clarifies the secondary action of fetching a specific context. It distinguishes itself from sibling tools by focusing specifically on Context JSONs, which are structured ontological frameworks.

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 explicit instructions on how to use the tool: 'Use 'list' to see available contexts, or specify a context name to fetch it directly.' This provides actionable usage guidance for the primary parameter. It does not explicitly mention alternatives or exclusions relative to sibling tools, but the tool's specific purpose makes the intended use clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral transparency burden. It discloses the return content (services, pricing, payment methods) and implies a read-only operation via 'Returns', but does not elaborate on behavior such as authentication needs, rate limits, or output format details. This meets the minimum viable level.

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 main action and followed by a purposeful usage note. No wasted words, and it is appropriately concise for a simple get tool.

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 (no parameters, no output schema, read-only nature), the description sufficiently covers its purpose and content. It could optionally mention that output is machine-readable, which it does, but lacks details on format or constraints, which are not critical for a get-only catalog.

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, so the description has no parameter details to convey. The baseline for zero parameters is 4, and the description appropriately omits any parameter-related text.

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 identifies the tool's function: it returns the full service catalog with specific content (services, pricing, payment methods). This specific verb+resource combination distinguishes it from sibling tools like get_agent_info and get_context_jsons.

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 states when to use the tool ('Use this to understand what Archangel Agency offers to AI agents'), providing clear context. However, it doesn't explicitly mention alternatives or when not to use it, though sibling tools are sufficiently distinct.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description's verb 'Returns' implies a read-only retrieval with no side effects, but it does not explicitly disclose behavior beyond that, such as default depth or whether content is static. However, as a simple retrieval tool, this is adequate.

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 concise sentences cover purpose and usage with no filler; the description is front-loaded with the action verb.

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 lacks an output schema, but the description indicates structured content and the depth parameter provides detail options. It's sufficiently complete for a simple retrieval tool, though it doesn't elaborate on the briefing's structure.

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 fully describes the depth parameter with enum values and defaults, so the description need not add more; the 100% schema coverage sets a baseline of 3, and the description adds no extra parameter 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 tool returns a specific briefing about Universal Language, with a distinct resource type. It identifies the content domain and distinguishes from sibling tool names by focusing on a unique research briefing.

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 explicitly mentions when to use it—for questions about UL, AI alignment, or UQPL—but does not discuss exclusions or alternative tools, yielding a clear but incomplete guideline.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.