vonage-documentation-mcp

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

No annotations are present, so the description carries full behavioral burden. It discloses that it gives a link or detailed information but does not clarify which, nor does it describe error behavior, response format, or potential limitations. This is a minimal disclosure for a reference lookup 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?

The description is two sentences, front-loaded with the core purpose, and immediately follows with usage 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?

The tool is simple with one well-described parameter, but the output is underspecified ('direct link or detailed information') and no output schema exists. It sufficiently guides when to use it but leaves ambiguity about what the response will look like, making it minimally acceptable.

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 a clear example-based parameter description, so the description doesn't need to add much. It reinforces that the query targets an endpoint but doesn't provide additional semantic detail beyond the schema. Baseline 3 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?

The description states a clear purpose: providing direct links or detailed info from Vonage API reference documentation for a specific endpoint. It differentiates from general documentation tools by mentioning highly technical endpoint-specific queries, but it does not explicitly name sibling alternatives.

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 explicitly says when to use it: 'Use this when the user's query is highly technical and related to a specific endpoint, like API parameters, request bodies, or response schemas.' It provides clear context but omits when-not-to-use or alternative tool names.

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 adds value by describing the output as 'complete, runnable' and noting it is for a specified language, but it does not explain potential limitations, input expectations for language specification, or any side-effect profile. This is a moderate addition beyond the tool's name and schema.

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 long, front-loaded with the core action and output, and every sentence adds valuable context. It is efficient and well-structured, avoiding unnecessary 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?

For a simple one-parameter tool with no output schema, the description provides the essential context: what it does, when to use it, and output expectations. It could be more explicit about whether the query must specify the programming language, but overall it is sufficient for the tool's complexity.

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 schema has 100% coverage for the single 'query' parameter, so the baseline is 3. The description adds a hint that the language is relevant to the snippet generation, but it does not directly elaborate on how the query should be structured or that it should include the language. No extra parameter-level detail is provided.

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 it generates a complete, runnable code snippet in a specified language for a given Vonage API task, using a specific verb and resource. It also distinguishes itself from sibling tools by noting it is the preferred choice when the user asks for a 'code snippet,' 'example,' or 'code in a specific language'.

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 explicitly states when to use this tool (when the user asks for a code snippet, example, or code in a specific language), giving clear triggers. However, it does not explicitly mention when not to use it or name alternative tools, so it lacks a full exclusion clause.

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 only says 'Searches' which implies a read-only operation, but it does not mention output format, limitations, or any side effects. The description adds minimal behavioral context beyond the tool's name.

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 concise sentences with no redundant wording. It front-loads the core purpose and then provides usage context, making it easy to parse quickly.

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 simplicity of the tool (one parameter, no output schema), the description covers the main function and usage context. However, it does not describe what the search returns or any behavioral expectations, which would be helpful for the agent to set user expectations or handle results.

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 schema provides 100% coverage for the single 'query' parameter with a clear description. The tool description adds no additional parameter semantics, so it correctly falls at the baseline of 3 for fully documented schemas.

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: searching official Vonage developer documentation for guides, tutorials, and API references. It identifies itself as the primary tool for general queries, which subtly distinguishes it from more specific sibling tools, though it does not name alternatives explicitly.

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 on when to use the tool: 'should be used when the user asks for information, documentation, or how-to guides.' However, it does not discuss when not to use it or direct to specific sibling tools for niche needs, leaving some ambiguity for the agent.

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 provided, the description carries the full burden. It discloses the type of information returned (installation instructions, supported features, version numbers) and the scoping constraint. While it doesn't explicitly state absence of side effects, 'Retrieves information' implies a read-only operation, and no contradictions 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?

The description is two sentences, front-loaded with the main verb and resource, and contains no filler. Every sentence earns its place, making it efficient 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 one parameter and no output schema, the description provides sufficient context: what it does, what it returns, and its scope of applicability. It slightly lacks explicit mention of expected query format, but the schema covers that.

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 schema covers the single 'query' parameter with a clear description (programming language of the SDK), achieving 100% coverage. The tool description adds no additional meaning beyond the schema, so the baseline of 3 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?

The description clearly states the verb ('Retrieves information') and resource ('a specific Vonage SDK'), and explicitly scopes the tool to SDKs themselves rather than underlying APIs. This distinguishes it from sibling tools like vonage_api_reference.

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 on when to use the tool ('queries focused on the SDKs themselves') and when not ('not the underlying APIs'). It stops short of naming the alternative tool for API queries, but the guidance is unambiguous.

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 are provided, so the description carries the transparency burden. It describes the type of information returned (troubleshooting steps, error explanations, debugging advice) but does not explicitly state limitations, non-destructiveness, or whether it can resolve the issue itself. This is adequate but not rich.

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 long, front-loaded with the core purpose, and every sentence adds significant value. It is 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 parameter, no output schema), the description fully covers what the tool does and when to use it. The content type of the response is clearly indicated, making it complete for an agent to select and invoke 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 sole parameter 'query' has a clear description in the schema (100% coverage). The tool description does not add additional semantic detail beyond what the schema already provides, so the baseline of 3 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?

The description clearly states the tool provides troubleshooting steps, error code explanations, and debugging advice for Vonage API issues. It uses specific verbs and resources, and distinctly differentiates from sibling tools like vonage_api_reference and vonage_docs_search.

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 explicitly states when to use the tool: when the user is reporting a problem, error, or something not working as expected. It does not explicitly mention when not to use it or name alternatives, but the when-to-use guidance is clear and helpful.

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 are provided, so the description carries full burden. It mentions that the tool 'provides a link', but does not disclose behavior around missing results, whether it returns multiple options, or any underlying API/web scraping behavior. The description adds some context beyond the schema but lacks depth for a no-annotation 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?

The description is two sentences, front-loaded with the core function, and contains no filler or redundant information. Every word earns its place.

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-like tool with a single well-documented parameter and no output schema, the description is largely sufficient. It covers what it does, what it returns (a link), and when to use it. It would be slightly more complete if it addressed failure behavior, but the simplicity of the tool makes this a minor gap.

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% with the 'query' parameter described as 'The topic of the tutorial or guide' with examples. The tool description does not add meaningful semantics beyond the schema; it only reinforces that the query should be a topic. Baseline 3 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?

The description clearly states the tool 'Finds and provides a link to a step-by-step tutorial or a blog post on the Vonage Developer blog', identifying both the verb and the resource. It also distinguishes from siblings by specifying tutorials/blog posts vs. other tool types like docs search or API reference.

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 guidance on when to use this tool: 'when the user asks for a "tutorial" or a "guide" on a specific topic.' This is clear context, but it does not mention when not to use it or name specific alternative tools, 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.

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

No annotations are provided, so the description must carry the full burden. It discloses that the tool 'finds and describes' content, implying a read-only operation, but it does not mention limitations, output format, or edge cases. The description is adequate but not deeply 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 consists of two sentences with no filler: the first states the function, the second gives usage context. Every word earns its place, 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 simple one-parameter tool with no output schema, the description covers purpose, usage context, and parameter semantics. It does not explicitly describe return format or failure behavior, but the phrase 'finds and describes' implies the output. Overall, it is adequately complete for a tool of this complexity.

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 describes the single parameter 'query' as 'The Vonage product (e.g., 'Video API', 'Voice API', 'Messages API')' with 100% coverage. The description adds no extra semantic meaning beyond the schema, so the baseline score of 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 function: finding and describing real-world use cases or customer stories for a specific Vonage product. Uses a specific verb ('finds and describes') and resource ('use cases or customer stories'), distinguishing it from siblings like vonage_api_reference or vonage_tutorial_finder.

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 explicitly says 'Use this when the user asks for examples of how a product is used in a specific industry or for a particular purpose,' providing clear usage context. However, it does not explicitly mention alternative tools 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.