Datailo: one MCP connection to tenders, EU regulation and law

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

The annotations declare readOnlyHint=true and destructiveHint=false, yet the description states 'action='revoke_key' ... revokes one,' which is a state-changing operation. This is a direct annotation contradiction, and the description cannot be trusted as written.

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 dense sentences with no filler. The purpose is front-loaded, and the security-relevant note 'never the key' 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?

Despite the action examples, the description omits output expectations, the irreversibility of key revocation, and any auth/session context beyond the schema. The annotation contradiction makes the behavioral context incomplete and potentially misleading for an agent.

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 only 33%, but the description compensates for the bare 'action' and 'key_prefix' parameters by showing exact usage ('action='keys'', 'revoke_key' with key_prefix). The 'session' parameter is already thoroughly described in the schema.

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 resource (the connected account) and gives concrete actions: 'keys' lists dat_ keys and 'revoke_key' revokes one. It clearly conveys what the tool does, though it does not explicitly compare itself with sibling 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 explains two action values but gives no guidance on when to prefer this tool over siblings such as datailo_connect or datailo_plan. No exclusions, prerequisites, or alternative routing 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 declare readOnlyHint, idempotentHint, and non-destructive, so safety is covered. The description adds valuable context beyond annotations: it explains that rows are rendered from live product cards and that services without a card show 'unavailable'. This helps set expectations about data freshness and missing data, which the annotations do not convey. A slight deduction for not explaining the 'session' parameter's role in this context, but that is covered in the schema.

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

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

The description is a single, efficient sentence that fronts the main purpose and enriches with the note about live rendering. It is compact and to the point, with no wasted words. The only minor issue is that it doesn't replace the need for parameter explanations, but as a standalone description it is 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?

The tool is read-only with no output schema, so the description adequately covers what the tool returns. However, given the low parameter coverage (7/10 params undocumented), an agent may not know how to use 'q' or 'audience' effectively, which could lead to incorrect invocation. The note about product cards helps but does not fully mitigate the parameter ambiguity. Overall, it is adequate for a browse 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 description coverage is only 33% (only 'session' has a description). The description does not explain the 'q' or 'audience' parameters at all. Since coverage is low, the description must compensate, but it does not: it only mentions the data returned and gives no hint about how 'q' filters or what 'audience' does. 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 clearly states it lists federated services with specific fields (name, job, datasets, tool/workflow counts, tiers, enabled status). It distinguishes itself from siblings like datailo_tools (which likely lists tools) by focusing on the service catalogue. However, it does not explicitly name a sibling to compare against, so it gets a 4 rather than 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?

It implies usage for browsing available services, but does not explicitly state when to use this tool versus alternatives like datailo_tools or datailo_describe. There is no explicitly stated 'when not to use' or alternative names, only implicit context from the field list. A clearer routing to siblings would improve this.

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 only include readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false, so the description carries the burden. It discloses that an email is sent with a confirm link and six-digit code, that a request_id is returned, that the account is created on first confirmation, and that nothing is charged. This is transparent, though it does not mention failure modes or what happens if the email is not confirmed.

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 concise enough and front-loaded with the purpose. It is structured logically: purpose, parameters, behavior, next step. Each sentence adds value, though the instruction to tell the user and then call verify could be slightly more compact, it is clear and not overly verbose.

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 two-step authentication tool with two parameters and no output schema, the description covers the essential information: purpose, parameters, the email behavior, the return of request_id, the need to call verify, and the no-charge/no-card aspect. It lacks details on error handling or the exact structure of request_id, but that is not critical for 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 0%, so the description must explain parameters. It mentions 'email' and 'language' and explains the language is the language spoken with the user, with examples ('lt', 'en', 'de'). It clarifies that language is optional via the default, and that email is sent in that language. This adds meaning beyond the schema's bare property definitions.

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: 'Sign in, inside the chat (needed to enable services and call their tools).' It explicitly identifies it as step 1 of 2 and differentiates it from the sibling datailo_connect_verify by describing the subsequent call. The verb 'sign in' and the resource 'Datailo' are specific, and the scope is unambiguous.

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 clear context for when to use this tool: 'needed to enable services and call their tools.' It also instructs the agent to tell the user to click the confirm link and then immediately call datailo_connect_verify(request_id), which defines the flow. It does not explicitly state alternatives (since there are none for sign-in), but the guidance is sufficient.

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 are sparse (no safety hints), so the description carries the burden and does so thoroughly. It discloses that the tool waits up to 45 s, returns a waiting status, requires repeated calls, issues a durable dat_ key (shown once), and provides a session value when needed. No contradictions with annotations; this is exemplary behavioral disclosure.

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 dense paragraph with no filler. It front-loads the purpose, then gives step-by-step instructions and edge cases. Every sentence earns its place, using emphasis (RIGHT AFTER) for critical timing. It is efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema and only two parameters, the description covers when to call, how to call, what to expect (waiting/success), retry logic, and post-success actions. It even addresses the client session edge case. Nothing an agent needs to call it correctly is missing.

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%, but the description explains both parameters: request_id is required (from step 1) and code is optional (if user reads it). It clarifies the semantics of 'no code' vs 'pass it as code'. This compensates fully for the missing schema descriptions.

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 the exact purpose: 'Finish signing in' and identifies it as step 2 of 2, clearly distinguishing it from the sibling datailo_connect (step 1). It specifies the action (verify sign-in) and the resource (connection session). An agent can immediately understand what this tool does and how it differs from its siblings.

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?

Explicit guidance: 'Call it RIGHT AFTER datailo_connect' with specific instructions on passing request_id and optionally code. It tells when to call again (if returns waiting) and what not to do ('do not ask the user to report the click'). It also explains post-success steps (carry on with user's request). This fully equips the agent to use the tool correctly.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds what the response contains but does not go beyond that into behavioral details like error conditions or open-world variability. No contradiction exists.

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, tightly written sentence that leads with the core purpose and then lists the contents of the product card. Every word contributes value, with no redundancy or 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?

For a simple read-only, one-parameter tool, the description is mostly adequate and usefully lists the output components despite the lack of an output schema. However, the missing semantics for the service argument and the absence of any usage guidance leave a notable gap that an agent must handle by inference.

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 only parameter, service, has no schema description and the description provides 0% coverage, yet it is required and lacks format guidance. There is no indication whether service expects a name, slug, ID, or some other identifier. The word 'service' in the description is the only hint, which is insufficient.

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 tool as returning a full product card for a single service, enumerating its contents: tagline, datasets, workflows, tiers, and docs. This distinguishes it from broader listing tools like datailo_catalog, even though the verb is implicit in the title. It is specific about resource and scope.

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 retrieving detailed information about one service, but it does not explicitly state when to choose this over siblings such as datailo_catalog or datailo_workflows. There is clear context for the single-service case but no exclusions or alternative routing.

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 destructiveHint=true and readOnlyHint=false, so the description's mention of 'revokes the hub's key' adds a specific mechanism but does not contradict annotations. The description adds the reassurance that data remains with the user, which is valuable context beyond the generic mutation warning. However, it doesn't disclose any side effects on connected workflows or other services. No contradiction.

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 with zero waste. It front-loads the purpose and includes the key behavioral guarantee ('Your account and data there remain yours.') in an efficient manner. The structure is exemplary.

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 mutation tool with destructiveHint=true and no output schema, the description covers the core action and reassures about data retention. The sibling context and the 'session' parameter are adequately handled through schema and naming. It could mention if the operation is reversible, but the 'disable' concept implies it can be re-enabled via 'datailo_enable'. Overall, it is complete enough for an agent to call 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%, with the 'session' parameter being fully described in the schema. The description itself does not elaborate on the 'service' parameter, but the schema title and the description's context make it clear that 'service' refers to the service to disable. Since the schema covers the nuanced session parameter well, the description doesn't need to add much, yet it provides enough context for the required parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Disable' and the resource 'a service', and explains the mechanism ('revokes the hub's key for it'). It distinguishes this from the sibling tool 'datailo_enable' by the opposite action, though it does not explicitly name the sibling. A clear and specific purpose.

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 implies the tool is used for disabling services but provides no explicit guidance on when to use it versus the sibling 'datailo_enable' or other tools. The description is clear on the action but not on selection criteria, which is acceptable since the tool name and title are self-explanatory, but it lacks an explicit 'when to use' statement.

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?

Beyond the annotations, the description discloses meaningful side effects: the hub links or creates an external account by email, stores an encrypted key, and enables keyless services as a trial. This gives the agent useful behavioral context about account linking and credential handling that annotations alone do not provide.

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 compact and front-loaded: purpose first, behavioral detail second, downstream consequence third. Every sentence contributes useful information and there is no 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?

For a tool with no output schema and low parameter coverage, the description is incomplete. It does not explain what the tool returns, how to populate 'plan', where to find valid 'service' identifiers, or what the 'session' parameter is for, leaving important gaps for an agent trying 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?

Schema description coverage is only 33%, and the description does not compensate. It gives context for the 'service' concept but never explains acceptable values for 'service' or the meaning of 'plan', and it omits 'session' entirely even though the schema documents that parameter.

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 and resource: 'Enable a federated service on your Datailo account.' It also distinguishes itself from siblings by explaining that datailo_tools and datailo_invoke will reach the enabled service through this connection, clarifying the tool's role relative to related operations.

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 when to use the tool: it is the prerequisite that lets datailo_tools and datailo_invoke reach a service. However, it does not explicitly state when to prefer this over siblings like datailo_connect or datailo_disable, and it offers no exclusions or alternative conditions.

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 include readOnlyHint=false, which aligns with the description's 'Call' implying mutation/action, and openWorldHint=true, indicating dynamic behavior. The description adds the return value ('Returns the service's own result') and references datailo_tool_schema for args, but does not disclose side effects, error handling, or rate limits, which would be valuable for an invoke 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 one sentence, concise, and front-loads the core functionality. It avoids redundancy with the annotations. It could be improved by adding a second sentence on when to use it, but it is efficient and informative.

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 complexity (dynamic invocation, 4 params, no output schema), the description covers the basic purpose and links to datailo_tool_schema, but lacks guidance on error handling, side effects, and prerequisites like enabled service or connection. The annotations provide limited context, so the description should carry more weight but does not fully address these 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 low (25%), with only the 'session' parameter described in the schema. The description adds meaning by indicating that 'service', 'tool', and 'args' are required, but it could provide more detail on the expected format of 'args' and clarify that 'service' and 'tool' correspond to entries from datailo_tool_schema. Despite this, it does compensate partially for the low schema coverage.

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 ('Call') and resource (a tool of an enabled service via the hub), and mentions it requires service, tool name, and arguments. It distinguishes itself from siblings like datailo_tool_schema, but does not explicitly contrast with datailo_describe or datailo_tools, which may also relate to tool information, though those are likely read-only.

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 it is the action tool for invoking tools, while siblings like datailo_tool_schema or datailo_describe are for inspection. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention prerequisites like needing an enabled service or prior connection.

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 non-read-only, non-idempotent, non-destructive behavior, and the description adds meaningful context beyond them: the beta is free, nothing is charged, and no checkout link is returned. That last point is genuinely useful because it tells the agent not to expect or promise a checkout URL after enroll. It stops short of describing what 'discontinue' actually does to a service's plan.

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, front-loaded with the action enumeration and followed by the beta-billing caveat. No filler, though the second sentence could be tightened into the first.

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 does well to disclose that no checkout link is returned, but it leaves gaps for a mutation-capable tool: the effect of 'discontinue', how 'tier' interacts with the free beta, and what 'service' should reference. Annotations cover the safety profile, so the remaining omissions are moderate rather than critical.

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 only 25% (the session param is the only one documented), so the description must compensate. It does enumerate the four valid 'action' values, which is real added meaning, but says nothing about the 'service' identifier format or the 'tier' parameter beyond the vague hint that no paid plan exists to choose.

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 the resource ('a service's plan') and enumerates the exact action modes (status | enroll | change | discontinue), so an agent knows the tool handles plan lifecycle operations. The leading verb 'See' undersells the mutating actions (enroll/change/discontinue), which creates mild ambiguity about read vs write, but the action list resolves it.

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 action enum implies what modes exist, but there is no guidance on when to choose this tool over siblings like datailo_account or datailo_catalog, and no prerequisites or exclusions. An agent must infer that plan management belongs here rather than being told.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds context about what the tool returns (orientation info, enabled/available services, next steps) but doesn't disclose details like whether it makes network calls or caches results. 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?

One sentence, front-loaded with the most important information ('Orientation for a new connection'), and every clause earns its place. The 'Call this first' instruction is a high-value addition at the end.

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 read-only orientation tool with one optional parameter and no output schema, the description covers what the tool does and when to call it. It could mention what the output looks like (e.g., a list or summary), but the annotations and simplicity make this a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the single optional 'session' parameter is fully documented in the schema. The description doesn't add parameter-specific meaning, but with full schema coverage, 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 clearly states this is an orientation tool for a new connection, listing what it covers: cross-service jobs, enabled/available services, and next steps. It distinguishes itself from siblings by being the entry point ('Call this first'), though it doesn't explicitly name a sibling alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Call this first,' which is strong usage guidance for a new connection. It implies this is the starting point before using other tools like datailo_plan or datailo_invoke, but it doesn't explicitly state when not to use it or name alternatives.

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, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds behavioral details like 'read live from each service' and ranking behavior, which is useful. However, it doesn't mention potential latency or that results depend on enabled services, leaving some gaps. It does not contradict 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 sentence that packs all essential information without fluff. It is front-loaded with the core purpose, followed by parameter details. Every clause adds value, and it is appropriately concise for a search 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 moderate in complexity with 5 optional parameters and no output schema. The description covers most aspects, but it doesn't explicitly state that the session parameter should be passed as per the schema, nor does it mention pagination or result format. However, given the annotations and schema, it is mostly complete 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?

Schema description coverage is only 20%, so the description must add meaning for the parameters. It does explain q, service, include_available, and limit, but the 'session' parameter is only described in the schema, not in the description. The description adds value for most parameters but misses the session parameter, which is critical for authentication in context.

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 that this tool searches tools from enabled services, using a query 'q' that can be words or a sentence. It distinguishes its purpose from siblings like datailo_describe or datailo_tool_schema by focusing on search across services, and it explicitly mentions ranking by matched words with name and title first.

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 explains how to use the tool: q takes words or a sentence, service narrows to one service, include_available previews additional services, and limit caps the list. It doesn't explicitly say when to use this instead of other tools, but the purpose is clear enough that an agent can infer it for discovery tasks. It lacks explicit exclusions or alternatives, but the context is strong.

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, idempotentHint, and destructiveHint=false, so safety is covered. The description adds the notion of 'enabled service' and 'how to call it', which provides some context, but it doesn't disclose return format, error behavior, or authentication nuances. Given the annotations, 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, front-loaded sentence with no filler. It is concise and to the point, though it could be slightly expanded to include critical details without becoming verbose.

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?

This is a meta-tool with no output schema, so the description should clarify what the returned schema looks like and how to use it. The phrase 'and how to call it' is vague and doesn't specify the return structure. It also doesn't explain the relationship to other Datailo tools, making it incomplete for an agent to use effectively.

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 schema coverage at only 33% (only 'session' has a description), the description carries little burden. However, it fails to explain what 'tool' and 'service' mean or how they combine to identify the target tool. The description says 'for one tool of an enabled service' but doesn't elaborate on the parameters, leaving the agent to guess.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides the full input schema for one tool of an enabled service, and how to call it. The verb 'schema' and resource 'tool' are specific, and the mention of 'enabled service' hints at a prerequisite. It distinguishes from siblings like datailo_tools (which likely lists tools) and datailo_invoke (which calls them), though it could be more explicit about the contrast.

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 is given on when to use this tool versus alternatives. It doesn't explain that this should be used to fetch a schema before invoking a tool, nor does it reference any sibling tools. The agent is left to infer the usage context.

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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value beyond annotations by disclosing the return structure (services to enable, stages with playbooks, checkpoints, work-product template) and that each step is annotated with the exact invoke call. This contextual behavior is not present in the structured fields.

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

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

The description is two sentences, with the core purpose front-loaded in the first sentence. The second sentence adds parameter-specific behavior and return contents, but it is somewhat dense and mixes usage guidance with output description. No wasted words, though the structure could be clearer by separating parameter semantics from return details.

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 read-only tool with annotations carrying the safety profile, the description provides a decent overview of what is returned but lacks explicit examples, parameter value constraints, and the exact output schema (no output schema exists). It also does not explain the relationship to datailo_invoke calls in enough detail for an agent to immediately construct a correct invocation without further exploration. Adequate but with clear 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 only 33% (only 'session' has a description). The description compensates partially by explaining that 'service' should be 'datailo' and that 'name' refers to a job name. However, it does not provide examples, allowed values, or format for 'service' beyond 'datailo', nor does it clarify how 'name' maps to a workflow external ID versus internal ID. The added meaning is useful but incomplete.

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 tool as returning one playbook's steps from the service, each annotated with the exact datailo_invoke call. It specifies the resource (one workflow) and differentiates from the sibling datailo_workflows by singular 'One' versus plural, so an agent can distinguish them without inspecting schemas.

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 a conditional hint ('service='datailo' with a job name returns the whole job') but never explicitly states when to use this tool instead of alternatives like datailo_workflows or datailo_invoke. No when-not-to-use guidance is provided, leaving the agent to infer the distinction from the name and 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 cover read-only, idempotent, non-destructive nature, so the description adds value by specifying the ordering (cross-service jobs first) and the enabled/disabled marking. This goes beyond the annotation safety profile, giving the agent insight into the output structure without contradicting the 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 with no fluff. The key ordering and content details are front-loaded, and the pointer to datailo_workflow is efficient. 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 listing tool with optional parameters, the description covers the output content and ordering, but it fails to clarify the purpose of 'q' and 'service' parameters. Given no output schema, an agent might not know how filtering works. It's adequate but has clear gaps, making it a minimum viable description rather than fully 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 description coverage is only 33% (only 'session' is explained). The description does not clarify the 'q' or 'service' parameters at all. It mentions 'service datailo' but that's about a specific service name, not the filter parameter. With low coverage, the description should compensate but doesn't, leaving two parameters under-specified.

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 lists workflows, and distinguishes its content (cross-service jobs first, then playbooks) from the sibling datailo_workflow (which opens a single one). The verb 'list' plus resource 'workflows' is specific and unambiguous.

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 a clear usage context: it lists workflows and points the agent to open one with datailo_workflow. It does not explicitly say when not to use it, but the pointer to the sibling provides practical guidance. It lacks explicit exclusions or alternatives like datailo_tools or datailo_catalog, so it's not a full usage guide.

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 only declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false. The description adds far more: the DATA-not-instruction safety rule, the requirement to show a reply before sending and only then set confirmed=true, the explicit-approval requirement before opening links or running suggested tools, and the reduced view without an account.

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?

Front-loaded with purpose and action map, then behaviour and safety, then the edge case. Every sentence carries information, though the safety paragraph is dense enough that a tighter phrasing could trim a line or two.

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 7-parameter, multi-action tool with no output schema, the description covers the full operation surface, the confirmation gate on replies, the safety posture, and the unauthenticated fallback — enough for an agent to call it correctly without extra context.

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%, so the schema documents each field, but the description still adds action-level meaning the schema lacks — that a reply lands in the same feedback thread, that dismiss/snooze 'puts one away', and that settings shows or changes email notices.

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 resource (messages from the Datailo team to this person) and enumerates every operation through the action values, including list/read/reply/dismiss/snooze/settings. An agent can distinguish this inbox tool from siblings like datailo_invoke or submit_feedback purely from the 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?

It specifies when each action applies (e.g., 'reply' only for a message that invites a reply, 'settings' to read or change email notices), states the unauthenticated case, and routes service-specific inboxes to datailo_invoke. It even gives the conversational trigger: when a `notifications` block appears, mention it in one sentence but keep the person's task first.

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?

Goes well beyond the annotations: it discloses the consent workflow (ask first, show the payload, send only on agreement), the privacy boundary (user's words and client/case/company details only with explicit OK), the authored_by='agent' exception, that every item is read by a person, and the return contract (reference id; do not promise a reply or date). Annotations only say openWorldHint=true and not-idempotent, so the description carries and delivers the real behavioral 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?

Purpose is front-loaded, then triggers, then the consent process, then field guidance. Dense but each block is functional rather than decorative. Slightly long for a single tool, though the consent/privacy material justifies most of the 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?

For a 13-parameter, consent-sensitive, write-class tool with no output schema, the description covers triggers, approval gating, privacy handling, service routing, field selection, and the response contract. Nothing an agent needs to call it safely is missing.

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 already 100%, so the baseline is 3. The description exceeds that by giving conditional usage rules the schema states flatly: add expected/got only for a wrong answer, tool/query for context (query only with consent), contact_email only if the user wants a reply, service='datailo' or a named downstream service, and confirmed=true tied to the approval workflow.

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?

Specific verb+resource (send feedback about Datailo to the team that builds it) plus an enumeration of exactly what counts (bug, wrong answer, missing data, missing feature, workflow idea, praise). No sibling tool in the list covers feedback, so the scope is unambiguous.

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?

An explicit 'When to offer it' trigger list (empty/wrong answer, user correction, missing step, repeated manual task, unmet need) plus guidance to offer once, in one sentence, with the user's task first. This is as close to operator instructions as a description gets.

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