Aggregates release notes for internal dashboards
Scored across 11 tools
Most tools have a distinct action (hash_input, text_to_speech, generate_image, view_dashboard), but batch_process and long_running_task both simulate long-running work and are easy to confuse, as are dynamic_tool and trigger_tools_list_changed. Descriptions clarify intent only partially, and nothing ties the tools to the stated 'Release Notes Feed' purpose.
Nine of eleven names follow a clean snake_case verb_noun pattern (generate_image, hash_input, summarize_text, turn_on_lights, view_dashboard). The deviations are long_running_task and dynamic_tool, which are noun phrases and read as vague rather than actionable.
Eleven tools sits comfortably in the reasonable range, and each one appears to demonstrate a distinct MCP capability (tasks, sampling, apps, dynamic tool lists). The count is fine; the issue is thematic coherence rather than volume.
For a server named 'Release Notes Feed' there is no tool to create, list, fetch, or publish release notes — the surface never touches the stated domain. As an MCP feature-demo toolkit it covers many capabilities, but as a release-notes server it is a dead end for any real workflow.
Simulates a batch processing operation (demonstrates MCP tasks feature). Use task augmentation to invoke.
| Name | Required | Description | Default |
|---|---|---|---|
| itemCount | Yes | Number of items to process (1-10) | |
| processingTimeMs | No | Time to process each item in milliseconds (min: 500, max: 5000, default: 1000) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It does disclose the key trait that the operation is simulated (no real side effects) and that task augmentation is required, but it says nothing about duration, progress reporting, cancellation, or what the caller receives when the simulated work finishes.
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 short sentences, front-loaded with the purpose and followed by the invocation constraint. The parenthetical about the MCP tasks feature is justified because it explains why the tool exists at all.
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 two fully documented parameters and no output schema, the schema side is well covered, but the description leaves the agent guessing about the return value, whether progress is streamed, and how this differs from the long_running_task sibling. Adequate for a demo tool, but not complete.
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% — both itemCount and processingTimeMs have ranges, defaults, and units documented in the schema itself. The description adds no meaning beyond that, 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?
States a specific verb and resource ("Simulates a batch processing operation") and clarifies it is a demonstration of the MCP tasks feature, which tells the agent this is a mock rather than production work. It does not, however, distinguish itself from the sibling long_running_task, which likely covers similar ground.
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?
"Use task augmentation to invoke" is a real invocation constraint and is the most useful sentence in the definition. But there is no guidance on when to pick this over long_running_task or batch-like siblings, and no statement of what happens if task augmentation is not used.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
A dynamic tool (suffix: not_set)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure, and it discloses nothing. It does not say whether the tool reads or mutates state, what side effects or permissions are involved, or what a call returns. The parenthetical "suffix: not_set" is a placeholder that conveys no behavior.
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 text is short, but its brevity reflects under-specification rather than efficiency. The only substantive content is a placeholder suffix value that carries no meaning for an agent.
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?
Even for a zero-parameter tool with no output schema, the description must at minimum state what the tool does, and it does not. With no annotations and no output schema to lean on, an agent has no information at all about this tool's behavior or 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 input schema defines an empty object with zero properties, so there are no parameters whose meaning could be added. Per the baseline for zero-parameter tools, this dimension is not a source of deficiency here.
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?
"A dynamic tool (suffix: not_set)" merely restates the tool name and appends a placeholder value; it names no verb, resource, or outcome. Nothing in the text distinguishes it from any of the ten siblings such as batch_process or summarize_text.
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?
There is no when-to-use guidance, no exclusions, and no named alternative among the siblings. An agent has no basis for choosing this tool over hash_input, batch_process, or any other sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generates an image based on the provided prompt
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The prompt describing the image to generate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It does not disclose output format, image dimensions, style controls, latency, cost, safety filtering, or whether generation is synchronous, leaving significant gaps for an image-generation 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 a single, front-loaded sentence with no unnecessary wording. It is appropriately sized for a one-parameter tool.
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 required parameter and no output schema, but image generation has behavioral complexity that the description does not address. It is minimally complete but leaves important operational details unspecified.
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%, and the single prompt parameter is already documented in the schema. The description adds no meaning beyond what the schema provides, 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 states a specific verb and resource: "Generates an image" based on a prompt. It is clear what the tool does, though it does not explicitly distinguish itself from siblings such as text_to_speech or summarize_text.
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 no guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. It merely restates the basic action without helping an agent choose it over sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generates a large text payload for testing client handling of big responses
| Name | Required | Description | Default |
|---|---|---|---|
| sizeKb | No | Approximate payload size in kilobytes (default: 1024, max: 10240 i.e. ~10MB) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full behavioral burden, yet it says nothing about the payload's content (random text, filler, structured?), whether it is deterministic, whether the output is plain text, or whether very large responses risk truncation or transport limits. The only behavioral signal is that a large result is returned, which is a significant gap for a tool whose whole job is to stress response handling.
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?
One short sentence with zero filler that front-loads the action and the resource. Nothing could be removed without losing information.
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 a single optional parameter, full schema coverage, and no output schema, the definition is serviceable but thin: an agent still doesn't know what the payload looks like, whether calling it has side effects, or how big responses are handled. It is adequate rather than complete for a stress-testing utility.
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% and sizeKb already documents its default, minimum, and maximum with the ~10MB conversion. The description adds no meaning beyond the schema, which is the correct baseline of 3 when the schema does the heavy lifting.
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 pairs a concrete verb with a concrete resource ("Generates a large text payload") and states the intent ("for testing client handling of big responses"), which is enough to tell it apart from the siblings, none of which produce bulk payloads. It stops short of a 5 only because it never explicitly disclaims the task-oriented siblings like batch_process or summarize_text.
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 testing client handling of big responses" implies the usage context, but there is no explicit when-to-use/when-not-to-use framing and no mention of alternatives. For a synthetic test-data generator, the intended scenario is inferable but not stated as a rule.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Computes the SHA-256 hash of the provided input string
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | The string to hash |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full behavioral burden. 'Computes the SHA-256 hash' implies a deterministic, side-effect-free transformation, which is the key behavioral fact, but it does not state the output encoding (hex vs base64, casing) or whether the operation has any limits.
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 twelve-word sentence with the action and algorithm front-loaded and zero redundancy. Nothing could be removed without losing information.
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 trivially simple (one required string), so the description is nearly sufficient, but with no output schema it leaves the return value unspecified — an agent cannot tell whether it receives a hex digest, a prefixed string, or a byte array.
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% and the single property is documented as 'The string to hash'. The description's phrase 'the provided input string' adds no 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?
States a specific verb ('Computes'), a specific algorithm ('SHA-256'), and the resource ('the provided input string'). None of the siblings (batch_process, summarize_text, generate_image, etc.) overlap with hashing, so no differentiation is required; an agent knows exactly what this does from the first clause.
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?
There is no explicit when-to-use or when-not-to-use guidance, and no alternatives are named. For a single-purpose utility with no plausible sibling overlap the intended use is strongly implied, but an agent gets no stated conditions (e.g., input size limits, encoding expectations) that would guide a decision.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Simulates a long-running task
| Name | Required | Description | Default |
|---|---|---|---|
| taskName | Yes | The name of the task to run |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing. It does not say whether the call blocks, how long it takes, whether it can be cancelled, whether it has side effects, or what it returns.
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 single sentence contains no filler, but it is under-specified rather than concise. Brevity here comes at the cost of any actionable information about invoking the tool.
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 (one required string parameter, no output schema), so little is required, but with no annotations the description should at least explain the simulated behavior and return value. Neither is present, leaving the agent unable to predict what a call does.
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% and there is a single parameter (taskName) already documented in the schema. The description adds no meaning beyond it, but with full schema coverage 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?
"Simulates a long-running task" essentially restates the tool name long_running_task with no additional specificity. It does not say what is simulated, what the task does, or how it differs from any sibling such as batch_process or generate_large_payload.
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?
There is no guidance on when to use this tool, when not to, or what alternatives exist. Siblings like batch_process or generate_large_payload could plausibly serve similar purposes, and the description gives no basis for choosing between them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Summarizes text using LLM sampling (demonstrates MCP sampling capability)
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to summarize | |
| maxWords | No | Maximum words in summary (default: 50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that summarization is performed via LLM sampling and that the tool demonstrates MCP sampling, but it omits practical traits such as dependence on client sampling support, latency or non-determinism, and absence of side effects.
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 with the action front-loaded. The parenthetical is compact and adds mechanism context, though it is slightly demo-oriented rather than invocation-oriented.
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 two-parameter tool with full schema coverage and no output schema, the description covers what the tool does and the key sampling mechanism. It could state prerequisites or output format, but those gaps are minor given the low 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 100%, and both parameters are documented in the schema. The tool description adds no parameter meaning beyond what the schema already provides, so the baseline score 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?
States a specific verb and resource: 'Summarizes text.' It also names the mechanism, LLM sampling, but does not define scope or limits. No sibling appears to overlap, but the description does not explicitly differentiate 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?
Provides no guidance on when to use this tool versus alternatives, no exclusions, and no prerequisites. The parenthetical about demonstrating MCP sampling is mechanism context, not usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Converts text to speech and returns a WAV audio file
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to convert to speech |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It does disclose the meaningful behavioral fact that output is a WAV audio file, which matters for downstream handling, but says nothing about input length limits, supported languages/voices, latency, or failure modes for a synthesis operation.
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 zero filler, stating both the action and the return format. Nothing could be removed without losing information.
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 synthesis tool with no output schema, the description covers the essential action and the return artifact. The remaining gap is input constraints (length, language, voice behavior), which is minor 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 coverage is 100% and there is a single required parameter documented as 'The text to convert to speech', so the schema does the heavy lifting. The description adds no constraints such as maximum length, encoding, or accepted character set beyond what the schema already says; 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?
States a specific verb ('Converts') and resource ('text to speech') plus the produced artifact ('WAV audio file'), so the operation is unambiguous. It does not distinguish itself from nearby siblings like generate_image or summarize_text, but none of them overlap enough to create real confusion.
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?
There is no guidance on when to use this tool, no prerequisites, and no mention of an alternative such as summarize_text for text-only workflows. The agent must infer usage entirely from the purpose statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Triggers a tools/list_changed notification to test dynamic tool updates
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It says a notification is emitted but doesn't disclose who receives it, whether it is fire-and-forget, whether it mutates server state, or what response (if any) the caller gets back.
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. It could be slightly sharper by stating the effect on connected clients, but nothing is wasted.
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 side-effecting notification tool with no annotations and no output schema, the description is thin: it doesn't say what happens after the trigger, whether clients must reconnect, or how to verify success. The zero-parameter simplicity offsets this only partially.
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 takes zero parameters, so the baseline is 4. The description correctly implies no input is needed, and the empty schema confirms it.
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?
States a specific verb (triggers) and resource (tools/list_changed notification) plus the testing intent. An agent can distinguish this from siblings like dynamic_tool or batch_process, though it doesn't explicitly name a sibling or describe what the notification contains.
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 'to test dynamic tool updates' implies the usage context (testing/demonstration), but there is no explicit when-to-use vs when-not-to-use guidance or mention of alternatives such as dynamic_tool, which sounds closely related.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Turns on the lights in a specified room
| Name | Required | Description | Default |
|---|---|---|---|
| room | Yes | The room to turn on lights in |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full burden. It does not disclose whether the operation is idempotent, what happens if the room is unknown, whether it requires authorization, or whether the light state persists — all relevant for a state-changing action.
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 well-formed sentence with no filler, and the resource and scope are front-loaded. The phrase 'in a specified room' slightly restates what the parameter already conveys, but it is not wasteful enough to penalize further.
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 tool with a fully described schema and no output schema, the definition is minimally sufficient. It falls short on behavioral context such as error conditions and state semantics, which matter even for a simple actuator.
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 single 'room' parameter is already documented as 'The room to turn on lights in'. The description adds nothing beyond that, so the baseline 3 for full schema coverage 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?
States a specific verb ('Turns on') and resource ('the lights') scoped to a room, which is unambiguous. Sibling tools are all unrelated utilities, so no differentiation is required, keeping this 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 gives no when-to-use or when-not-to-use guidance and names no alternatives. An agent must infer from the name alone that this is the tool for activating room lighting.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Opens an interactive dashboard (demonstrates MCP Apps extension)
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional filter query |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full behavioral burden and does not meet it. It does not say whether the dashboard is read-only, whether it renders UI versus returning data, whether any state is mutated, or what the caller receives back.
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?
It is short and front-loaded with the action, which is good, but at one sentence plus a parenthetical it is arguably under-specified rather than concise. The parenthetical is meta-commentary about the feature demo rather than information that helps invocation.
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 structurally simple (one optional param, no output schema, no nesting), so the description's brevity is defensible. However, it leaves the core unknown — what the dashboard displays and what the call returns — unaddressed for a tool whose whole point is an interactive UI.
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 single optional 'query' parameter is fully documented in the schema (100% coverage), so the schema already carries the meaning. The description adds nothing about what the query filters or its syntax, making this a baseline adequate case rather than a value-add.
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 gives a concrete verb and resource ('Opens an interactive dashboard'), so an agent knows exactly what the tool produces. It does not differentiate against siblings, but the siblings listed are unrelated utilities (hash_input, text_to_speech, summarize_text), so the risk of confusion is low.
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?
There is no explicit when-to-use or when-not-to-use guidance, and no named alternative. The parenthetical 'demonstrates MCP Apps extension' faintly implies a demo/dev context, but an agent must infer that rather than read it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
batch_processdynamic_toolgenerate_imagegenerate_large_payloadhash_inputlong_running_tasksummarize_texttext_to_speechtrigger_tools_list_changedturn_on_lightsview_dashboard
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