mcp-server

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

With no annotations provided, the description carries the full behavioral burden, and it does this well: it discloses the scheduling engine, the returned artifacts (picture, dates, critical path, link), export/save capabilities, deadline marker semantics, and the instruction to always show the link. The only minor gap is that it does not explicitly state whether or how the chart is persisted server-side.

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 long but dense and every section earns its place: the core purpose is front-loaded, authoring rules are actionable, and the sibling-tool distinction is at the end. It loses a point only because the 'YOU author the plan' block 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?

For a complex tool with no output schema, the description is remarkably complete: it covers auth, input construction rules, dependency semantics, phase grouping, deadlines, return values, link behavior, exports, and when to choose the alternative tool. An agent has enough context to select and invoke this 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 high at 86%, and the description still adds substantial semantic value: dependencies are 0-based positions of earlier tasks, typed links use { task, type, lag }, independent tasks should run in parallel, phases use isPhase and parent, milestones have duration 0, and deadlines are markers rather than hard constraints. This is far beyond what the schema alone conveys.

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 opens with a specific verb-resource pair: 'Create a Gantt chart from a task list.' It goes beyond a generic creation statement by specifying input (task list), output (chart, dates, critical path, link), and the absence of auth requirements. This clearly separates it from the sibling schedule_project tool.

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 final sentence gives explicit routing guidance: use create_gantt when the user wants a chart to open/export/save, and use schedule_project instead for what-if date math where nothing should be stored. This is concrete, contrastive, and leaves no ambiguity about tool selection.

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 behavioral disclosure burden. It clearly states that the operation retrieves a read-only template and what the result contains. It does not describe edge-case behavior like invalid slugs, but for a simple getter this is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no filler. It communicates the target, scope, return content, and downstream use in an efficient and readable way.

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 one-parameter retrieval tool, the description covers the main operational need: what is returned and how the result is used. Since no output schema exists, more detail about the exact structure of 'task list with durations' could help, but the description is still sufficient for selection and 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?

Schema description coverage is 100%, so the schema fully documents the slug parameter. The description repeats 'by slug' but does not add substantive semantic detail beyond the schema's own reference to list_templates.

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 and resource: 'Get one industry template by slug', and clarifies the content returned ('task list with durations'). It also distinguishes itself from list_templates by emphasizing retrieval of a single template.

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 clearly implies when to use this tool: when you need a specific industry template to adapt before passing it to create_gantt. It does not explicitly mention when not to use it or name alternatives, but the workflow context is sufficiently clear.

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

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

With no annotations, the description carries the behavioral burden. It communicates a read-only listing behavior and the content returned, which is sufficient for a zero-parameter list operation; it does not claim side effects or require caveats like pagination.

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 fluff: the first defines the listing behavior, the second gives the follow-on workflow. Useful information is front-loaded.

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 list tool, the description covers what is returned (templates with task counts and typical durations) and what to do next. It does not spell out the exact return shape, but with no output schema that is 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?

The tool has zero parameters and the schema fully reflects that, so the baseline 4 applies. There is nothing for the description to add about parameters.

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 ('List') and a specific resource ('LoopGantt's industry project templates') while adding output details (task counts and typical durations). The workflow reference to get_template and create_gantt immediately distinguishes this discovery action from the sibling creation/fetch 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 clearly places list_templates at the start of a workflow: pick a template, fetch with get_template, adapt, then create_gantt. It does not explicitly state when not to use it or contrast it with schedule_project, so it misses the full 'alternatives' bar, but the intended 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the tool computes without storing anything, implying a safe, side-effect-free operation, and lists the computed outputs. It could add more detail about failure modes or edge cases, but the core behavioral traits are 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?

Three sentences, each earning its place: the first delivers the core function and outputs, the second gives usage examples, the third names the alternative. Front-loaded and free of filler.

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?

Without an output schema, the description names the key return values (dates, critical path, float, project end), which is sufficient for an agent to know what to expect. It also covers when to use the tool and how it relates to create_gantt. Minor missing details about output formatting or performance are not critical given the tool's simplicity.

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 86%, so the schema already documents most parameters. The description adds only that the input shape matches create_gantt and that 'name' is optional. This is minor value beyond the schema, so the 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 states a specific verb ('Compute') and resource ('a schedule'), names the exact outputs (dates, critical path, float, project end), and explicitly distinguishes it from create_gantt by noting the same input shape but different purpose. There is no ambiguity about what this tool does.

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 gives concrete example questions the tool answers ('what is the critical path?', 'when would this finish?', 'how much slack does X have?') and explicitly directs users to the sibling tool create_gantt when they need a chart. The when-to-use and when-not-to-use guidance is clear and direct.

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