AI tools that run on your own machine: what each does, installs like, and costs.
Scored across 3 tools
Each tool has a clearly distinct purpose: one explains pricing/access, one retrieves details for a single tool, and one lists all tools. There is no overlap or ambiguity between them.
The names follow a consistent verb_noun pattern (get_access_and_pricing, get_tool, list_tools). The only minor deviation is that 'get_access_and_pricing' is a compound noun phrase rather than a simple noun, but it remains readable and predictable.
Three tools is on the low end but appropriate for a small catalog/information server. It feels slightly thin for a server that presumably manages a collection of tools, but the scope is narrow enough that the count is defensible.
The server covers listing, detail retrieval, and access/pricing information, which covers the core read-only use case. However, there are no operations for searching, filtering, or managing tools, and no obvious way to get pricing for a specific tool without reading the access doc.
How access works: which tools are free, what membership costs and unlocks, and the price floor for having a tool customised. Read this before telling anyone what something costs.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 describes the tool as informational, implying it is read-only, but it does not explicitly state that it has no side effects or that it is safe to call. Since the tool name begins with 'get', it is likely a query, but the description omits an explicit statement about non-destructive 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 description is two concise sentences, front-loaded with the core purpose. It efficiently communicates the tool's role and includes a usage directive without unnecessary fluff. 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?
Given the tool's simplicity (0 parameters, no output schema), the description is complete. It clearly enumerates what information the tool provides (free tools, membership costs, price floor) and gives usage context. Nothing critical is missing for an agent to call 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?
The tool has zero parameters, and the input schema is empty. According to the rubric, the baseline for 0 parameters is 4. The description adds no parameter-specific details because there are none to describe; this is appropriate and does not detract from the definition.
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: to explain access and pricing, specifically covering which tools are free, membership costs and unlocks, and the price floor for customisation. This is specific and distinguishes it from siblings like get_tool and list_tools, which focus on retrieving tool details or listings rather than pricing/access.
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: 'Read this before telling anyone what something costs.' This tells the agent when to use the tool, but it does not mention alternatives or explicitly state when not to use it. The directive is clear enough for a dedicated pricing/access tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
One tool in full: what it does, how it installs, what it runs inside, and how to get it.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The tool slug from list_tools |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral burden. It does specify what the result contains, which is the primary disclosure for a read-style tool, but it stops short of stating any operational traits such as error behavior for an unknown slug, authentication requirements, or whether the returned data is a live snapshot. For a benign get operation 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?
A single sentence with no filler, and the scope is front-loaded ('One tool in full') before the enumerated content. The phrasing is slightly cryptic but efficient; every clause earns its place by describing a distinct aspect of the returned object.
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 no output schema, the description must stand in for the return shape, and it does enumerate the four content areas an agent can expect. For a 1-parameter get tool this is reasonably complete, but it omits failure semantics (nonexistent slug), any mention of authorization, and a concrete sense of the response format, leaving minor but real gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single slug parameter, so per rubric the baseline is 3. The schema's own description ('The tool slug from list_tools') adds useful cross-tool context, and the description's 'One tool in full' reinforces that slug selects a single record. However, the tool description adds no parameter-level detail beyond what the schema already provides.
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 conveys that the tool returns a complete profile of a single tool, enumerating the specific facets covered ('what it does, how it installs, what it runs inside, and how to get it'). This clearly differentiates it from list_tools (which lists tools) and get_access_and_pricing (which focuses on access and cost), even though no sibling is named. Not a tautology; a concrete scope of returned data.
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 guidance, no exclusions, and no mention of alternatives. The only usage hint ('The tool slug from list_tools') lives in the input schema rather than the description, so an agent gets no explicit direction on how to sequence it with its siblings or when it would be the wrong choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Every Own Your AI tool: what it does, which platform it runs on, and whether it is free, members-only, or still coming. These run on the user's own machine.
| Name | Required | Description | Default |
|---|---|---|---|
| status | No | Optional filter: featured, free, paid, coming-soon |
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 mentions that tools run on the user's own machine, which is a useful behavioral note, but it does not disclose whether the tool returns cached data, if it requires network access, or if it lists only currently available tools versus those in development. For a read-only list tool, this is a moderate gap given no 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 one sentence that packs in the core information (scope, content, platform, pricing availability) and a second sentence adding the local-machine context. It is front-loaded with the essential purpose. No redundancy, and it earns its length.
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 optional parameter, no output schema, no nested objects), and the description covers the purpose and the key contextual note about local execution. However, it does not specify whether the list is sorted, if it includes deprecated tools, or how the 'status' filter interacts with the listing (e.g., does it filter by all listed attributes or just pricing?). For a simple tool, this is borderline adequate but leaves minor 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?
The schema already describes the 'status' parameter with an enumerated list of possible values (featured, free, paid, coming-soon), so schema coverage is 100%. The description does not add any additional semantics beyond what the schema provides, so a baseline 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 resource ('Every Own Your AI tool') and the information provided (what it does, platform, pricing availability). It distinguishes from siblings by focusing on listing all tools, whereas get_tool likely focuses on a single tool. A slight deduction for not explicitly contrasting with siblings, but the purpose is evident.
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 browsing all tools, and the optional 'status' filter suggests when it might be used to filter by status. No explicit exclusions or alternatives are given, but the context of siblings like get_access_and_pricing and get_tool suggests this is the list-all entry point. Usage guidance is implicit rather than explicit.
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.
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