abcommunity

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds the output categories, which clarifies the nature of the analysis but doesn't disclose other behavioral details like prerequisites or failure modes; it's consistent with the read-only annotation.

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 gets the core purpose across without waste. It could earn more by adding a hint about alternative inputs (e.g., repo_url vs repo_snapshot), but as written it is appropriately concise.

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?

With 7 optional parameters, zero schema descriptions, and 12 sibling tools, the description is too thin to ensure correct invocation. It doesn't state which inputs are needed, how they relate (e.g., repo_url vs repo_snapshot vs project_description), or when to prefer this over recommend_stack/plan_project_sprints. The presence of an output schema covers return values, but the input side remains under-specified.

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 0% and the description doesn't explain the 7 parameters. While repo_url and project_description are self-evident, goals, constraints, repo_snapshot, preferred_stack, and target_platforms are left undefined, so an agent cannot know how to populate them effectively.

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 uses a clear verb ('Understand') with a concrete resource (project idea/repo summary) and lists specific outputs (product type, stack, stage, modules, risks, open questions). However, it does not explicitly differentiate from siblings like recommend_stack, which also touches stack, so it stops short of a 5.

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 no guidance on when to choose this tool over siblings such as recommend_stack, plan_project_sprints, or map_resources_to_project. No when-to-use, when-not-to-use, or alternative conditions are mentioned, which is a significant gap given the large sibling list.

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 and destructiveHint=false, so the safety profile is covered. The description adds behavioral context beyond the annotations by committing to a factual, criteria-based output and explicitly disclaiming subjective winners, which shapes the agent's expectations.

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 sentences with no filler; the main verb and resource appear first and the criteria list is compact. It is appropriately sized for the tool's scope.

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 has a single parameter, an output schema, and annotations covering side effects. The description covers purpose, scope, comparison dimensions, and stance, so an agent has everything needed 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?

With 100% schema coverage, the refs parameter is already described as 'Slugs or ids to compare'. The description reinforces the min/max by saying 'two to five MCPs', but adds no new semantic detail such as example slug formats or default behavior.

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 names a specific operation ('factual side-by-side comparison') with a clear resource scope ('two to five MCPs') and enumerates the comparison axes. The closing 'No subjective winner is chosen' distinguishes it from recommendation-oriented siblings like find_mcp_for_task and recommend_stack.

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 for comparing explicit MCPs, but it never states when to prefer it over alternatives or mentions exclusions. An agent must infer that this is not for finding or searching MCPs.

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 flag this as read-only/non-destructive/open-world. The description adds process detail: capability detection, marketplace search, and a recommendation-plus-alternatives response with reasoning. 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?

Two sentences plus an example, with the key instruction front-loaded and no filler. The example earns its place by demonstrating the expected input format.

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 annotations, output schema, and the fact that most parameters are optional and self-descriptive, the description covers the essential workflow and expected output. It lacks only explicit pointers to sibling tools or behavior when no MCP matches, which are non-critical for invoking the 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?

The description adds meaning to the required task parameter by framing it as a natural-language goal statement and providing a concrete example. However, with schema coverage at 33%, the five optional preference parameters (pricing, security, deployment, open source) receive no explanation, so the description only partially compensates.

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 uses specific verbs ('detects', 'searches', 'returns') tied to a clear resource (MCPs in the marketplace) and includes a concrete Gmail/Linear example. It clearly separates this capability-discovery tool from siblings like search_resources or compare_mcps.

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 tells the agent exactly how to use it – describe the desired AI behavior in natural language – and what happens next. It does not explicitly name sibling tools for exclusion, but the natural-language capability-matching context is clear enough to avoid confusion with direct resource search or comparison tools.

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, openWorldHint=true, and destructiveHint=false, so the safety profile is covered without the description. The description adds that the output is role-specific and scoped to a sprint or area, but it does not explain whether the tool merely returns generated text, how it incorporates project_context, or any other behavioral nuance. This is acceptable 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 a single front-loaded sentence with no filler or repetition. It is appropriately concise for a simple generator tool, although the brevity does leave semantic gaps in other dimensions.

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?

An output schema exists, so return-value details are not required, but the description still fails to explain parameter semantics, usage context relative to sibling tools, or how the inputs are used. With five optional parameters and zero schema descriptions, an agent has too little guidance to invoke the tool correctly in a nuanced scenario.

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 0%, so the description must compensate for the five undocumented parameters. It only hints at 'role' through 'role-specific' and 'sprint or area' through 'sprint', but it says nothing about resources, project_context, or project_description. The agent gains little parameter understanding beyond what the bare property names already imply.

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 specific verb and resource: 'Create role-specific agent instructions for a project sprint or area.' It identifies a distinct deliverable and scope, which separates it from generic sibling tools like analyze_project or compare_mcps. However, it does not explicitly contrast itself with closely related tools like generate_config_bundle or plan_project_sprints, so it is clear but not maximally differentiated.

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 phrase 'for a project sprint or area' loosely implies the context in which the tool is used, but there is no explicit guidance about when to choose this tool over siblings such as plan_project_sprints or recommend_resources_for_sprint. No when-not-to-use conditions or alternative tool recommendations are provided.

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, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the behavioral context that the output is a 'bundle' of notes, checklist, steps, and verification steps, and that it is 'safe' (non-mutating). It does not disclose details like whether the bundle is returned as a file, text, or structured data, but the output schema exists to cover that. 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 a single, compact sentence that front-loads the core purpose and lists the bundle contents efficiently. It earns its place with no filler. It could arguably add a bit more parameter context, but as a concise statement it is 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 has 4 parameters, an output schema, and 12 siblings, the description is adequate but not complete. It explains what the tool produces but not how the parameters shape the output, when to prefer it over get_installation or generate_agent_blueprint, or what 'safe' means operationally. The output schema covers return structure, but the missing parameter semantics and usage guidance leave 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 description coverage is 0%, so the description carries the full burden for parameter meaning. The description mentions 'selected resources' and 'client-specific', which maps to selected_resources and client, but it does not explain project_context, project_description, or how they influence the bundle. With 4 parameters and zero schema descriptions, the description only partially compensates.

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 specific verb ('Create') and resource ('client-specific MCP config notes, env checklist, install steps, and verification steps'), which clearly distinguishes it from sibling tools like analyze_project or find_mcp_for_task. However, it doesn't explicitly name a sibling alternative or contrast itself with one, so it falls just short of a 5.

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 usage context: it is for generating a config bundle for selected resources, and the word 'safe' hints at a non-destructive, advisory purpose. But it does not state when to use this tool versus alternatives like get_installation or generate_agent_blueprint, nor does it mention any exclusions or prerequisites. The usage guidance is implied rather than explicit.

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 signal read-only, non-destructive, and open-world behavior. The description adds meaningful behavioral guarantees beyond those annotations: it never fabricates commands and, when nothing is recorded, explicitly says so and points to official docs. This is valuable anti-hallucination 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?

Two sentences with no wasted words. The first sentence front-loads the full expected return contents, and the second adds the important no-fabrication behavior. Both sentences earn their 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?

With only 2 parameters, a 100%-covered schema, an output schema present, and read-only/non-destructive annotations, the description plus structured data fully equips an agent to select and invoke the tool correctly. The anti-fabrication guarantee closes the main real-world risk.

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%: the schema already documents 'resource' as a slug/id and 'client' as a client-specific target. The description does not add extra parameter-level detail 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 uses a specific verb ('Return') and names the exact resource and payload categories: installation method(s), config example, package info, transports, auth requirements, and source references. This clearly distinguishes it from siblings like get_resource and search_resources by focusing specifically on installation instructions.

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 makes clear when the tool is appropriate: when an agent needs official installation instructions or related configuration/auth details for a resource. It does not name an explicit alternative or exclusion, so it misses full 5-level guidance, but the context 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the read-only nature is covered. The description adds that the action choice is based on the sprint roadmap and current state, but it does not disclose assumptions, failure modes, or behavior when required context is absent.

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?

A single front-loaded sentence with no filler or redundancy. It efficiently communicates what the tool returns and what information the result is based on.

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 has three parameters with no schema descriptions, and the description leaves the roles of state, project_context, and project_description mostly implicit. Even with an output schema present, the input-side semantics are too underspecified for an agent to know exactly what to provide.

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 0% and the state and project_context schemas are empty objects, so the description needed to explain the parameters. It loosely connects 'current state' to state and 'sprint roadmap' to project_context, but it never explains project_description or the expected structure/keys inside the nested objects.

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 uses a specific verb ('return') and object ('next most useful action for a project'), and names the inputs that drive the result ('sprint roadmap and current state'). It conveys the core purpose clearly, though it does not explicitly differentiate from sibling tools like plan_project_sprints or analyze_project.

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?

When to use it is implied: call it when you have a project with a sprint roadmap and current state and need the next most useful action. However, there are no explicit prerequisites, exclusions, or comparisons to alternatives such as analyze_project or plan_project_sprints.

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 and destructiveHint=false. The description adds that the result is 'complete structured marketplace metadata' and enumerates the covered dimensions, which is consistent with the annotations and gives useful context about the tool's scope. It does not discuss edge cases like missing identifiers, but the annotation coverage lowers the burden.

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 with no filler. The list of metadata categories is long but each item adds useful information about what the tool returns. It earns a 4 rather than a 5 because the enumeration could be tightened without losing meaning.

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 output schema covers return shape and annotations cover safety. The main gap is invocation semantics: with no required parameters, the description should clarify that exactly one of slug or resource_id is expected and what happens otherwise. This is a real gap for correct 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?

With 0% schema description coverage, the description must carry parameter meaning. It does map slug and resource_id to 'by slug or id', but it leaves ambiguity about whether one is required, what happens if both are supplied, and value formats. This is only partial compensation for the schema's silence.

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 uses a specific verb ('Return') and identifies the resource ('complete structured marketplace metadata') and the lookup modes ('by slug or id'). It also enumerates the metadata categories, making the tool's scope concrete. It does not explicitly contrast with siblings like search_resources or get_installation, so it stops short of a 5.

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 for retrieving full metadata for a known resource, but it never states when to prefer it over siblings such as get_installation or search_resources, and gives no exclusions or alternative conditions. Usage context is present but only by implication.

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 readOnlyHint=true and destructiveHint=false. The description adds value by explaining that the tool discusses config/risk and how to apply resources, which is useful behavioral context beyond the annotations. It is consistent with the read-only nature.

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 sentence that clearly states the core purpose upfront. It is efficient and free of filler, though it could be more structured with explicit sections.

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 4 parameters including a nested object, no required params, and zero schema descriptions, the description is insufficient. It does not explain what inputs mean nor provide usage context, leaving significant gaps despite having an 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 description coverage is 0%, so the description must compensate for the absence of parameter documentation. It does not mention limit, focus_area, project_context, or project_description at all, leaving agents to guess their meanings from names 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?

The description uses a specific verb ('Explain') and names the target resource ('ABCommunity resources apply to a project') and covers key dimensions (when, how, config/risk). It is clear and distinct enough from siblings like search_resources or recommend_resources_for_sprint, though it does not explicitly name them.

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?

No guidance on when to use this tool versus alternatives such as recommend_resources_for_sprint or find_mcp_for_task. The description only states what it does, not the conditions that should trigger its use or exclusions.

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 and destructiveHint=false, so the safety profile is covered. The description adds what the tool produces (sprints with tasks, acceptance criteria, risks, next actions), but it does not disclose other behavioral traits such as how it handles missing context or whether it persists anything. 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 a single, front-loaded sentence with no filler words. It efficiently communicates the core purpose, though it sacrifices useful detail about parameters and usage.

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 has six parameters, zero schema descriptions, and no usage guidance, so the description is not sufficient for an agent to invoke it correctly. While the output schema covers return values, the input side is severely under-specified 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?

Schema description coverage is 0%, and the description does not mention any of the six parameters (team_size, time_horizon, sprint_length, delivery_style, project_context, project_description). The description therefore adds no meaning beyond the raw schema, leaving the agent without guidance on how to populate these fields.

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 uses a specific verb ('Break') and a clear resource ('a project into execution-ready sprints'), and enumerates the concrete outputs: tasks, acceptance criteria, risks, and next actions. This clearly differentiates it from siblings like analyze_project or get_next_project_action, which address different planning concerns.

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 should be used when a project needs to be decomposed into sprints, but it does not explicitly state when to prefer this tool over alternatives such as recommend_resources_for_sprint or get_next_project_action. There are no exclusions or alternative routing cues.

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 and destructiveHint=false, so the safety profile is covered. The description adds that the tool recommends across multiple resource types (MCPs, skills, tools, APIs, frameworks), which is useful. However, it doesn't disclose how recommendations are generated, whether they are ranked, or what the output structure looks like. With annotations covering safety, a 3 is appropriate.

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 sentence that is concise and front-loaded with the action and resource types. It earns its place, though it could add a bit more context without becoming bloated.

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 has 5 parameters, 0% schema description coverage, and an output schema. The description is too thin to guide an agent on how to fill parameters like project_context or focus_area, or what the output will contain. Given the complexity (nested objects, multiple resource types), the description is incomplete.

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 0%, so the description must compensate for the 5 parameters. It does not explain the meaning of limit, sprint_id, focus_area, project_context, or project_description. The description only mentions 'one sprint in a project roadmap,' which hints at sprint_id but leaves the other parameters' semantics entirely to the schema. This is a significant gap.

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 specific verb ('Recommend') and resource ('MCPs, skills, tools, APIs, and frameworks') for a sprint in a project roadmap. It clearly distinguishes the tool's purpose from siblings like find_mcp_for_task or recommend_stack, though it doesn't explicitly name a sibling or contrast itself.

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 usage context: when you need resource recommendations for a sprint. However, it doesn't explicitly state when to use this tool versus alternatives like recommend_stack, map_resources_to_project, or find_mcp_for_task. The sibling list provides context, but the description itself offers no routing 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 declare readOnlyHint=true and destructiveHint=false. The description adds value by disclosing a non-obvious behavioral trait: it does not pad the stack with unnecessary MCPs and reports uncovered capabilities. This is transparency about the tool's honesty and completeness, which is useful and not contradicted by any annotation.

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 three concise sentences with no fluff. It front-loads the primary purpose and output, then adds two behavioral clarifications. Every sentence earns its place and there is 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?

The description covers the main input (multi-step workflow) and output components (roles, order, required/optional, uncovered capabilities). With an output schema present, return-value details need not be repeated. The only minor gap is that it doesn't mention the optional parameters beyond listing them in the schema, but those are self-explanatory from names. The description is adequately complete 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?

Schema description coverage is low (20% – only 'task' has a description), so the description should compensate for the other four parameters. It does not elaborate on 'platform', 'pricing_preference', 'deployment_preference', or 'open_source_preference' — their meaning is left to names alone. The description mentions 'workflow' in the context of 'task', but provides no additional semantics for the optional inputs.

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 with a specific verb ('recommend'), a precise object ('the set of MCPs needed'), and a scope ('for a multi-step workflow'). It explicitly lists output components (roles, order, required vs optional) and differentiates it from siblings like 'find_mcp_for_task' (single-task) and 'compare_mcps' (comparison), so an agent can tell it apart without ambiguity.

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 usage ('For a multi-step workflow') but does not explicitly contrast with alternatives or provide when-not-to-use guidance. It does not name sibling tools or state that it should be used instead of a single-task finder when the workflow has multiple steps, leaving the selection rules to inference.

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 establish readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds useful behavioral detail beyond that: ranked results, human-readable match quality, and source links, plus the filter axes. 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?

Three tight sentences: purpose and scale in the first, filter dimensions in the second, and output behavior in the third. It is front-loaded, has no filler, and every sentence 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 10-parameter optional search tool with an output schema, the description covers scope, filters, and return value character. It stops short of explaining filter interaction or routing among siblings, but it is sufficient for a caller to invoke the tool 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?

Schema coverage is 50%, and the description reinforces the meaning of most filter parameters (capabilities, resource_type, platform, pricing, deployment, open_source, verified). However, it does not clarify 'official', 'limit', or 'query' semantics, nor how filters combine, so the description only partially compensates for the schema gaps.

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 verb 'Search' and the resource—ABCommunity's catalog of 12,000+ AI resources—while enumerating resource types and filter dimensions. It does not explicitly differentiate from siblings like find_mcp_for_task or recommend_resources, but the catalog-scale framing makes the purpose reasonably distinct.

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 usage: use this tool whenever you need to browse or filter the ABCommunity catalog by capabilities, type, platform, pricing, deployment, or status. However, it provides no when-not-to-use guidance or explicit alternatives, which matters given several recommendation and search siblings.

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