Archangel Agency MCP

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

No annotations provided, so description carries full burden. It adequately describes return values (agent card contents) but omits other behavioral traits like caching, rate limits, or authentication requirements.

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?

Single dense sentence with zero waste. Front-loaded with action verb, immediately specifies data structure (A2A agent card), enumerates key fields, and identifies the subject entity.

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?

Appropriately complete for a simple zero-parameter metadata tool. Compensates for missing output schema by listing returned fields, though could specify data format (e.g., JSON) or freshness guarantees.

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?

Zero parameters present, establishing baseline 4. No parameter documentation needed; description correctly focuses on return value semantics rather than inventing parameter explanations.

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?

Excellent specificity with verb 'Returns' + resource 'A2A agent card', and clearly distinguishes from siblings by identifying the specific entity (Jono Tho'ra / Archangel Agency) and content type (skills, capabilities, payment methods, contact info).

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?

Provides implied usage through clear description of returned data, but lacks explicit when-to-use guidance or comparisons to siblings like get_service_catalog or get_context_jsons.

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?

No annotations provided, so description carries full burden. Adds valuable domain context (AI consumption, ontological frameworks) but omits operational details: error handling for invalid context names, caching behavior, or whether results are static/dynamic.

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 well-structured sentences. Front-loaded with action (Returns). Second sentence provides actionable usage guidance. Domain jargon ('ontological frameworks,' 'Universal Language primitives') is necessary for this specific tool's context.

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?

Adequate for a single-parameter retrieval tool with 100% schema coverage. Explains the 'list' vs 'fetch' return patterns. However, lacks description of return structure (JSON format, schema) since no output_schema is provided.

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 coverage is 100% with detailed enum description ('list' vs specific values). Description mirrors schema guidance without adding syntactic details, format examples, or semantics of individual context names (e.g., what 'archangel_agency' represents). Baseline 3 appropriate for high schema 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?

Clear verb ('Returns') and specific resource ('Context JSON briefing documents'). Distinguishes from siblings by defining these as 'structured ontological frameworks built on Universal Language primitives,' differentiating from get_universal_language_briefing (the primitives themselves) and other utilities.

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?

Explicitly documents the dual-mode operation: 'Use 'list' to see available contexts, or specify a context name to fetch it directly.' Clearly maps parameter values to behaviors. Lacks explicit sibling comparison (when to use vs get_universal_language_briefing).

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, description carries full burden. Adds useful behavioral hints ('machine-readable', 'full' catalog) and return content details. However, lacks disclosure on safety profile (read-only vs destructive), caching, rate limits, or performance characteristics expected for an unannotated tool.

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 well-structured sentences with zero waste. First sentence defines the operation and return payload; second provides usage intent. Information is front-loaded and appropriately sized for the tool's simplicity.

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 zero-parameter read operation without output schema, the description adequately covers what is returned (services/pricing/payment methods) and why to use it. Could improve by noting if the result is cacheable or if there are usage costs, but sufficient for agent selection.

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?

Input schema has 0 parameters with 100% coverage. Per scoring rules, 0 parameters establishes a baseline of 4. Description correctly implies no filtering is needed by stating 'full' catalog, aligning with the empty parameter schema.

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?

Excellent specificity: verb 'Returns' + resource 'service catalog' + detailed contents ('available services, pricing, and payment methods'). Clearly distinguishes from sibling tools (agent_info, context_jsons, language_briefing) by focusing on agency service offerings.

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?

Provides clear usage context: 'Use this to understand what Archangel Agency offers to AI agents.' effectively signals when to use it. Lacks explicit 'when not to use' or named alternatives, but the sibling differentiation is implicit in the specific scope.

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?

No annotations provided, so description carries full burden. 'Returns' implies read-only retrieval, and 'structured content' hints at format. However, lacks explicit disclosure of idempotency, caching behavior, or safety guarantees that annotations would typically provide. Does not contradict any annotations (none exist).

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 high-density sentences with zero waste. First sentence defines the resource and nature of content; second sentence defines applicability. No redundancy with schema or title.

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?

Appropriate for low complexity (1 optional param, no nesting). Description adequately covers what is returned (research briefing on geometric pattern language) and its use cases. Lacks output schema but implies text/content return; sufficient for a simple retrieval tool.

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%, providing detailed file specifications ('llms.txt (~2KB)', 'llms-full.txt (~15KB)'). Description mentions no parameters, but baseline 3 is appropriate when schema fully documents the single 'depth' parameter.

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?

Excellent specificity: 'Returns the Universal Language research briefing' provides exact verb and resource, while 'formally derivable geometric pattern language' precisely scopes the content. Distinct from siblings (get_agent_info, get_context_jsons) by targeting specific research content rather than agent metadata or generic context.

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?

Provides clear positive guidance: 'Useful for answering questions about UL, AI alignment through ontological context, or UQPL.' explicitly states applicable domains. Lacks explicit negative constraints ('do not use for...') or alternative routing, but sufficiently indicates when to invoke.

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