VerifyAX MCP

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

The description reinforces the destructive hint with 'permanently deletes' and 'cannot be undone', and adds that it returns a confirmation. It does not contradict the idempotent hint, though it does not explain idempotency 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?

Two concise sentences that front-load the key information. Every word adds value, 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 simple deletion tool with one parameter and no output schema, the description is sufficiently complete. It could specify the format of the confirmation, but the current text is adequate.

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 has 100% coverage with a description for agent_uuid. The description adds 'by its uuid', which is redundant. No additional parameter meaning is provided beyond 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 clearly states the action (permanently deletes), resource (agent), and method (by uuid). It distinguishes well from sibling tools like evaluate_agent or list_recent_runs, none of which involve deletion.

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 (when you want to permanently delete an agent) and notes the irreversible nature, but does not explicitly state when not to use or list alternatives. However, given the unique deletion purpose among siblings, this is clear enough.

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 indicate destructiveHint=true and idempotentHint=true. The description adds value by specifying irreversibility and the failure condition, and notes that a confirmation is returned. No contradictions.

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

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

The description is three concise sentences, each adding value: action, constraints, and return behavior. No wasted words, front-loaded with the main purpose.

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 delete tool with one parameter, the description covers irreversibility, precondition, and return. Without an output schema, mentioning confirmation is helpful. Could elaborate on what the confirmation contains, but it's adequate.

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

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

With 100% schema coverage and the parameter description already included in the schema, the tool description adds minimal new information about the parameter. The schema already describes 'The uuid of the scenario to permanently delete.' 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?

The description clearly states the action 'permanently deletes a scenario' with the specific identifier 'by its uuid', which distinguishes it from sibling tools like generate_scenario or list_compatible_tags. The verb and resource are explicit.

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 important usage context: the deletion is permanent and 'fails if simulation runs still reference the scenario'. This tells the agent when not to use it (if referenced). It could be improved by stating when to use it, but the prerequisite is clear.

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

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

Adds substantial behavior beyond the sparse annotations: discloses runtime duration (30s–30min), the dual execution model (task-capable clients get a pollable task handle; others block), and the internal sequence of operations (preview cost → run simulation → wait → return scores). This is exactly the kind of context annotations cannot convey.

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

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

Three tightly packed sentences, front-loaded with purpose and runtime expectation before parameter guidance. Every clause earns its place with 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 long-running, potentially blocking operation, the description covers runtime, client-dependent execution model (critical for avoiding timeouts), the internal workflow, and required inputs. Nothing essential for correct invocation is missing despite the absence of an output schema.

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

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

Schema coverage is 100%, so all four parameters are already documented in the schema. The description calls out timeout_minutes's override semantics and its 1–240 range, which mirrors the schema, and adds no new meaning for num_runs or the uuids. 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+resource ('Runs an agent against a scenario and evaluates the result end to end'), and distinguishes itself from siblings like preview_run_cost and get_run_details by covering the full execute-then-evaluate flow. An agent can immediately tell this is the end-to-end evaluation entry point.

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?

Implies usage by stating the required inputs ('Give it an agent uuid and a scenario uuid') but never explicitly says when to use this vs. preview_run_cost or get_run_details, nor when-not to use it. Context is implied rather than stated.

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 by disclosing latency (30s–2min), the dual execution model (pollable task handle for task-capable clients vs blocking for others), batch semantics, and the failure mode (structured error, e.g. incompatible tags). Annotations only say it is a non-read, non-destructive write, so the description carries meaningful extra behavioral context.

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

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

Three front-loaded sentences with no filler: capability, execution timing/model, and return/error behavior. It is dense but each clause carries information; the batch detail is arguably the only overloaded part.

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 compensates by explaining return values (uuid, batch uuids, structured error). For a 9-parameter tool it leaves context_prompt and name semantics to the schema, but the critical complexity (batching, task handling, failure) is covered.

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 78% and parameters like name, context_prompt, and scenario_type already carry their own descriptions, so the schema does the heavy lifting. The description restates the num_scenarios/tag_pool relationship rather than adding new meaning, which is the expected baseline when coverage is high.

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 ("Generates a new test scenario") and enumerates the two valid scenario types, so the action is unambiguous. It does not, however, differentiate itself from siblings like list_scenarios or preview_run_cost, leaving the agent to infer the boundary.

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?

Gives clear context: single vs batch mode, the tag_pool prerequisite for num_scenarios > 1, and points to list_compatible_tags when picking tags. There are no explicit when-not-to-use statements or sibling routing, so it stops short of a 5.

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=true, consistent with a fetch operation. Description adds that it includes status and evaluation results when available, but doesn't elaborate on other behavioral traits like idempotency or lack 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?

Two concise sentences, front-loaded with the primary purpose, and no wasted words.

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 could benefit from more detail about the structure of the returned details. It only mentions status and evaluation results, leaving other 'full 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%, so the schema already documents the single parameter. The description adds no additional meaning beyond what is 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?

Clearly states it fetches full details of a single simulation run, distinguishing it from sibling tools like list_recent_runs which lists runs, and evaluate_agent which evaluates agents.

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?

Explicitly advises 'Use after a run to inspect scores and outcome,' giving clear context for when to use. Lacks explicit alternatives or when-not-to-use guidance.

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

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

Annotations already provide readOnlyHint=true, so the description builds on that by detailing pagination, a cap on events, and the return fields (total count, product area breakdown, spend). It also notes that spend is conditional on API reporting. This adds meaningful behavioral context beyond the annotation.

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

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

Three sentences covering purpose, filters, pagination, and return values. No fluff, front-loaded with key information. Every sentence adds value.

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

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

Given the tool has 10 optional parameters and no output schema, the description explains the main return values and pagination behavior. It misses details on some filters (e.g., 'failed', 'product_area') but overall provides a sufficient understanding of what the tool does and returns.

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

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

The description groups parameters into categories (time range, simulation, scenario, job) but does not explain individual parameters like 'failed' or 'product_area' beyond what the schema already provides. With schema description coverage at 60%, the description only partially compensates.

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

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

The description clearly states the tool summarizes VerifyAX usage events with optional filters, using a specific verb and resource. It distinguishes itself from siblings like list_scenarios or get_run_details by focusing on aggregation (count, breakdown, spend) rather than listing raw events or runs.

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: for a summary over a time range or for specific simulations, scenarios, or jobs. It does not explicitly state when not to use or name alternatives, but the context is clear. No exclusion criteria are provided.

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

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

Annotations already declare readOnlyHint=true, and the description aligns by describing a read-only list operation. It adds context on return fields, which is useful. No further behavioral details are needed given the annotation coverage.

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 cover purpose, filtering, and return fields. No redundant information, and the key points are front-loaded. Every part is necessary.

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 low complexity (3 optional params, no output schema), the description covers the main functionality and return fields. Lacks explicit pagination explanation, but the schema parameters imply it. Overall sufficient for the tool.

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

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

The description explains the agent_type parameter and its enum values, but does not mention limit or offset parameters. With schema description coverage at 33%, the description partially compensates but could be more complete on pagination 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 the tool lists AI agents in the workspace, with an optional filter by connector type. It specifies the return fields (uuid, name, type, URL), making it distinct from sibling tools like register_agent or delete_scenario.

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 this tool (to list agents, optionally filtered), but does not explicitly provide when-not-to-use or alternatives. It lacks guidance on pagination or cost implications, but the context is clear enough for basic usage.

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. The description adds value by detailing the return structure (name, category, description) and special flag for QnA tags. No contradictions.

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 action. Every sentence adds meaningful information with no waste.

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 full schema coverage, annotations, and description covering output structure and special behavior, the description is fully adequate for this simple tool.

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

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

Schema coverage is 100% with the only parameter scenario_type having a clear description. The description does not add any additional semantics beyond the schema, so 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 the tool lists skill tags for a given scenario type (info_exchange or interview). It uses a specific verb ('lists') and resource ('skill tags'), and the purpose 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?

Explicitly says 'Use this before generating a scenario to pick valid tags,' providing clear when-to-use guidance. It does not list when not to use or alternatives, but the sibling generate_scenario is an obvious counterpart.

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, so description's 'lists' is consistent. Description adds filter options and return fields but does not mention pagination behavior, default ordering, or rate limits. 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?

Two concise sentences front-loading the core purpose and available filters. No fluff, every word adds value.

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 9 parameters and no output schema, the description covers the main filters and returned fields. However, it omits pagination details (limit/offset) and default sort order, which are useful for an agent to know for correct invocation.

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

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

Schema coverage is 56%; description summarizes filter categories (status, agent, etc.) but does not explain individual parameters like limit/offset beyond what is in the schema. Adds marginal value.

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?

Directly states the function: listing simulation runs with optional filters and specifies the returned fields. Clearly distinguishes from siblings like get_run_details (single run) and list_agents.

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?

Lists available filters but does not explicitly guide when to use this tool versus siblings (e.g., get_run_details for a single run, preview_run_cost for cost estimation). No when-not or alternative tool mentions.

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, so description of read-only nature is redundant. Adds return fields but lacks details on pagination, rate limits, or sorting. Adequate but minimal added value.

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?

Single sentence, front-loaded with key information, no wasted words. Highly 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?

Given no output schema, description covers return values (uuid, name, type, status). For a simple list tool, it provides essential context. Missing pagination behavior, but overall complete for basic usage.

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?

Description adds meaning for scenario_type and status filters beyond schema (only status has description). However, limit and offset are not explained; schema coverage is 25%, so description partially compensates but misses pagination details.

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?

Description clearly states it lists test scenarios in a workspace with optional filters and specifies returned fields (uuid, name, type, status). Distinguishes from sibling tools like list_recent_runs.

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?

Explains optional filtering by type and status, providing clear context. No explicit when-not-to-use or contrast with siblings, but the description is sufficient for correct use.

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=true, and description reinforces it's only an estimate without executing. Discloses return fields (estimated credits, balance, pending committed spend). 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?

Two sentences, front-loaded with main purpose, then specific details. Every sentence adds value; 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?

Given no output schema, description adequately states what is returned. For a preview tool with read-only semantics and well-documented params, it's sufficiently 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%, so description adds limited additional meaning. Reiterates timeout_minutes range (1–240) and its effect on estimate, but other parameters (num_runs, agent_uuid, scenario_uuid) are not elaborated beyond 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?

Clearly states it estimates credit cost before running an agent against a scenario. Verb 'estimates' + resource 'credit cost' is specific. Distinguishes from sibling tools like evaluate_agent or get_run_details.

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?

Explicitly says 'before triggering it', indicating when to use. Mentions optional timeout_minutes affects estimate. No explicit when-not or alternatives, but context provides sibling names.

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?

The description adds value beyond annotations by detailing that A2A agents are verified for reachability and DIRECTLINE/MCP agents are probed before creation. It also warns that credentials transit the conversation. Annotations already indicate a non-read, non-destructive 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?

The description is concise (3-4 sentences), front-loaded with the main purpose, and efficiently covers probing behavior, return information, and a security note. No unnecessary words.

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 (17 parameters, nested objects, no output schema), the description covers essential aspects: purpose, connector-specific behavior, security, and return value. It could be improved by mentioning error conditions or idempotency, but it is sufficiently complete for effective use.

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 only 35% schema description coverage, the description adds some context by explaining connector types and sensitivity of credentials, but it does not individually describe the majority of parameters. The description partially compensates for 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 explicitly states 'Registers an AI agent in VerifyAX given its name, connector type...' providing a specific verb and resource, and distinguishes the tool from sibling tools which focus on scenarios, runs, and evaluation.

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

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

The description clearly indicates when to use the tool (registering agents) and provides context on probing behavior for different connector types. However, it does not explicitly state when not to use it or mention alternatives.

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