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

Description aligns with annotations: destructiveHint=true (removes from workspace), idempotentHint=true (archiving again likely harmless). Adds context about recoverability, enhancing transparency.

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, effect, and use case. No wasted words; front-loaded with action.

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 simple archive operation with clear annotations. No missing context about behavior, parameters, or output.

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 covers 100% of parameters with descriptions. Description does not add information beyond schema, so baseline score applies.

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?

Description clearly states the verb 'Archive' and the resource 'Gamma' with specific types (presentation, document, etc.). Distinct from sibling tools like gamma_generate or gamma_get_status.

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?

Describes the effect ('removed from active workspace but retained for recovery') and use case ('declutter your dashboard'). Does not explicitly state when not to use, but context is 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?

Annotations indicate readOnlyHint=false (modification) and destructiveHint=false. The description adds behavioral context by explaining the 'Remix feature' and mentioning the waitForCompletion parameter, which reveals polling behavior. No contradictions with annotations.

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 well-structured with a summary, parameter list, returns note, and example. It is concise but could be slightly more compact. The front-loading of the purpose is effective.

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 has 6 parameters (1 required), nested objects, and no output schema, the description is adequate but lacks details about the return value when waitForCompletion is false. The returns note 'Same as gamma_generate' is vague without knowing gamma_generate's output.

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 parameters. The description lists parameters with names and an example, but adds little extra meaning beyond the schema. The example provides context for usage, but doesn't deeply enhance semantic understanding.

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 purpose: 'Create new content by remixing an existing Gamma template.' It uses a specific verb ('Create') and resource ('content by remixing template'), and distinguishes from sibling tool gamma_generate by specifying 'remix' versus likely create-from-scratch.

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 some context ('Useful for creating variations of proven templates') but lacks explicit guidance on when to use this tool versus gamma_generate or other siblings. No when-not-to-use or alternative tools are mentioned.

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?

Annotations indicate a non-read-only, non-destructive action. The description adds valuable behavioral context: generation consumes credits, waitForCompletion behavior, default AI-determined card count, and return status. No contradiction with annotations.

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 well-structured with sections for formats, parameters, returns, and examples. It is front-loaded with the primary action. While comprehensive, it remains clear and easy to scan, though it could be slightly tighter.

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 no output schema, the description covers return fields (generationId, status, url, title). It addresses formats, sizes, parameters, and credits. It misses error handling or authentication details but is sufficient for proper invocation.

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 is empty (0 parameters, 100% coverage), so the description carries the full burden. It thoroughly explains all parameters, including required ones, options, defaults, and examples, which is far beyond the 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?

The description clearly states the tool creates new presentations, documents, webpages, or social posts. It specifies the verb 'create' and lists supported resources, distinguishing it from sibling tools like gamma_from_template and gamma_list_themes.

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 on when to use the tool (creating new content from text) and references gamma_list_themes for theme selection. It lacks explicit exclusions or alternatives for some siblings, but examples and parameter descriptions give adequate guidance.

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?

Annotations already provide read-only, idempotent, non-destructive hints. Description adds polling behavior and return values, enhancing transparency beyond annotations.

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?

Well-structured with purpose, usage, parameters, returns, example. Every sentence contributes, no fluff.

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?

Comprehensive for a status-checking tool: covers all parameters, behavior, return values, and example. No gaps given absence of output schema.

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% so baseline is 3. Description adds context on polling mechanism and example usage, adding value over schema alone.

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?

Description clearly states 'Check the status of a Gamma generation' with specific verb and resource, distinguishes from siblings like gamma_generate by focusing on status polling.

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 says 'Use this tool to check if a previously started generation has completed', providing clear context. Could mention when not to use, but example and sibling differentiation suffice.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds details about what the tool checks (API key, reachability, account status) and its output, which is fully transparent and consistent with annotations.

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 highly concise: one sentence stating purpose, one sentence giving usage advice, and a bullet list of use cases. 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.

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), the description fully covers what the tool does, when to use it, and what it returns. Annotations provide safety context, and the description compensates for lack of output schema by stating return fields.

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%: the only parameter 'response_format' is fully documented with enum values, default, and description. The tool description adds no additional information about the parameter, 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 explicitly states the tool checks if the Gamma MCP server and API are reachable and working, and specifies it returns API key validity, reachability, and account status. This clearly distinguishes it from sibling tools like gamma_generate or gamma_list_themes.

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 explicit guidance: 'Use this to verify your configuration is correct before running other tools.' It also lists specific use cases like confirming server startup, troubleshooting connections, and verifying API key validity.

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?

Annotations already cover readOnly, idempotent, etc. The description adds value by detailing the return structure (items, total, hasMore, nextOffset) and the example, which clarifies behavior. No contradictions.

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?

Well-structured with clear sections (what, why, PARAMETERS, RETURNS, EXAMPLE). Front-loaded and no unnecessary words. Could be slightly more concise by integrating the example.

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 list tool with good annotations and schema coverage, the description adequately covers purpose, integration hint, and return format. Complete for its complexity level.

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 covers all three parameters (100% coverage), so baseline is 3. The description's PARAMETERS section omits response_format but mentions it in the example, adding minor value. Redundant for limit and offset.

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 lists Gamma folders and mentions their use for organizing content. It differentiates from siblings like gamma_list_themes by focusing on folders. Not a perfect 5 because it doesn't explicitly contrast with other list tools.

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 explains that folder IDs can be used with gamma_generate, providing context for use. However, it lacks explicit guidance on when not to use this tool or alternatives beyond that hint.

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?

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by detailing the return structure (items with id, name, description, total, hasMore, nextOffset) and the optional response_format parameter, providing full transparency beyond annotations.

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 well-structured with sections for explanation, parameters, returns, and example. It is front-loaded with the main purpose and avoids unnecessary repetition. However, it could be slightly more concise (e.g., combining parameter descriptions).

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 no output schema, the description thoroughly explains the return values (items, total, hasMore, nextOffset) and ties the tool into the broader workflow with gamma_generate. All parameters are documented, and the example aids understanding. No gaps.

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% (all parameters have descriptions), so baseline is 3. The description adds an example that shows how parameters work together (e.g., { limit: 10, response_format: 'markdown' }), which provides concrete usage context that the schema alone does not.

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 lists available visual themes for Gamma content, and explains how themes control visual styling. It distinguishes itself from siblings by specifically mentioning it returns theme IDs used with gamma_generate.

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 explicit guidance to use returned theme IDs with gamma_generate's themeId parameter, implying this tool should be used before generating content. It lacks explicit when-not-to-use or alternative tool comparisons, but the context is 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?

Beyond the minimal annotations (readOnlyHint=false, destructiveHint=false), the description adds valuable behavioral details: sends email invitations, recipients receive a link, and waitForCompletion behavior (max 5 minutes). It does not cover all edge cases like invalid emails or rate limits, but adds meaningful context.

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 purpose and includes an example and note, but it is not fully concise: it is missing two parameters (response_format, waitForCompletion) from the parameter list, making it incomplete. It could be tighter while covering all parameters.

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 5 parameters, no output schema, and moderate annotations, the description is mostly complete but lacks mention of the response_format parameter (which controls output format). It also does not explain failure scenarios or retries. The return section is present but incomplete relative to schema options.

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 adequate descriptions for all parameters. The description lists three parameters (generationId, emails, message) and includes an example, but omits response_format and waitForCompletion from the bullet list, though waitForCompletion is mentioned in the note. Overall, the description adds marginal value beyond the 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?

The description clearly states 'Share a generated Gamma with recipients via email.' It uses a specific verb ('share') and resource ('Gamma'), and distinguishes itself from sibling tools like gamma_generate and gamma_archive which have different purposes.

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 instructs to ensure the generation is complete before sharing and mentions using waitForCompletion: true to handle this automatically. While it doesn't explicitly list when not to use or name alternatives, it provides clear context for appropriate usage.

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