Younanix Memory MCP

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

With no annotations, the description carries the burden. It discloses an authentication/ownership requirement and implies a mutation (complete/update). However, it does not discuss side effects, reversibility, or potential conflicts (e.g., if a task is already finalized), which leaves some transparency gaps for a write 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 a single, well-structured sentence that front-loads the primary action and includes a terse prerequisite. Every word adds 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?

The tool has 5 parameters, nested objects, no output schema, and no annotations. The description explains the core purpose and a few parameters, but does not cover status enum semantics, the structure of verification/blockers, or expected response/errors. It is sufficient for basic invocation but not fully complete for a tool with this complexity.

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

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

Schema description coverage is 0%, so the description must compensate. It explicitly names 'final result, verification evidence, and blockers' which maps to final_result, verification, and blockers parameters. However, it does not elaborate on the required task_run_id or the status enum values, leaving some parameter meaning implicit.

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: 'Complete or update the final status of a Hermes task run' with specific elements (final result, verification evidence, blockers). This distinguishes it from siblings like start_task_run which initiates a run, and query tools which read 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 provides clear context on when to use the tool (when completing/updating a task run's final status) and notes a prerequisite ('Requires x-agent-key and task ownership'). It does not explicitly mention alternatives or exclusions, but the context is unambiguous enough to differentiate from starting or querying tasks.

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

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

With no annotations provided, the description carries the full behavioral burden. It discloses the response envelope (schema_version 2, status, structured lessons, coded errors, next_actions) and the preview limitation, which is substantial. It does not mention rate limits or side effects, but read-only search tools rarely need further 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?

Two sentences, no fluff. The core purpose is stated immediately, followed by the return envelope and the critical limitation. 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?

The description is complete for a search tool: it describes the return format, error handling (coded errors), and next_actions. The output schema (per signals) further covers return structure. Only minor gaps exist, such as not explicitly stating the default max_results, but that is in the 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 description coverage is 100%, so all parameters (query, verbosity, max_results) are fully documented in the schema. The description adds no additional parameter-level detail beyond the high-level envelope mention, which matches the baseline of 3 for high 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?

States a specific verb (Search) and resource (Younanix engineering lessons) and describes the output envelope. It explicitly frames itself as a 'free preview surface' and names query_lessons_premium as the full-access alternative, but does not differentiate from the sibling query_lessons_full, leaving some ambiguity about how it differs from that tool.

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

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

Provides an explicit when-not: full lesson bodies require query_lessons_premium or the paid agent-query endpoint. However, it does not mention when to use query_lessons_full instead, so the guidance is incomplete for distinguishing among all siblings.

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

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

With no annotations, the description carries the safety burden. It clearly warns that invalid auth leads to a payment_required envelope with next_actions, which is a behavioral trait. It also mentions the schema_version, implying structure stability. This goes beyond a simple 'returns data' statement, though it could disclose limitations like rate limits or whether results are sorted.

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 total, both information-dense. The first states the output and structure, the second the auth requirement and fallback behavior. No fluff; each clause 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 rich output schema and clear parameter schema, the description covers auth, output format, and key variants. The tool is low-complexity (single required parameter), so nothing critical is missing. The description compensates for the lack of annotations.

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 baseline is 3. The description adds value by explaining the verbosity effect ('compact trims lesson bodies and drops narrative prose') and mentioning the envelope structure, which helps understand how max_results interacts. This is more than schema descriptions alone.

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 a specific verb ('Returns') and the resource ('full Younanix Arete V7 lessons'), and adds a distinctive detail—the agent-native envelope with fields like root_cause and lesson_learned—that distinguishes it from sibling tools such as query_lessons. 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?

It specifies the required authentication (x-agent-key header) and hints at the context (admin-issued key), which is valuable. However, it does not compare to siblings like query_lessons_premium or query_lessons, so when to use this tool over them is not stated. Still, the auth condition is a clear usage constraint.

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

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

With no annotations, the description carries the burden of behavior and does usefully disclose the x-agent-key free path, the x402 payment requirement including amount and chains, and the payment_required envelope with a pay_x402 action. It does not explicitly state that the operation is read-only or describe success-response shape, but the output schema covers the response.

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 packs payment, auth, and response behavior into a single dense sentence without fluff. The heavy punctuation and jargon reduce readability slightly, but every clause carries substantive integration information.

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

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

The payment flow and authentication are well specified, and the output schema covers return structure. Missing context is the core purpose and differentiation from sibling query tools, which an agent needs to select and invoke the tool with confidence.

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

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

Schema description coverage is 100%, so the schema fully documents query, verbosity, and max_results. The description adds no parameter-level meaning, which is acceptable at the baseline since the schema already supplies descriptions and enum constraints.

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 is a noun phrase ('Premium Younanix Arete brief') rather than an explicit action like 'queries lessons' or 'returns a brief', leaving the core function to be inferred from the tool name and the 'query' parameter. It does not distinguish this tool from sibling tools query_lessons or query_lessons_full.

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 payment and authentication mechanics but gives no guidance on when to choose this tool over query_lessons or query_lessons_full. There is no stated condition, prerequisite, or alternative comparison.

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 discloses an important side effect ('update the parent run reflection summary') and an auth requirement ('Requires x-agent-key and task ownership'). Since no annotations are provided, the description carries the burden, but it omits details such as whether the update is an append or overwrite, idempotency, or failure 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, both front-loaded with the core action and the auth requirement. There is no redundant or irrelevant text; 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?

Given the tool's complexity (9 parameters, nested objects, no output schema, no annotations), the description is too sparse. It explains the high-level purpose but fails to clarify how to populate the required fields or what the tool returns/does beyond the parent summary update, making reliable invocation difficult.

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%, and the description adds no explanation of any of the 9 parameters. It does not mention task_run_id, outcome, or the semantic meaning of fields like evidence, lessons_used, or younanix_feedback_action, leaving agents to guess from bare property names.

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: 'Record the structured self-reflection for a Hermes task run' and 'update the parent run reflection summary.' This specific verb and resource scope distinguish it from sibling tools like record_subagent_trace and report_skill_outcome, though it does not explicitly name alternatives.

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: it is used for Hermes task runs and updates the parent run's reflection summary. It also notes the prerequisite of x-agent-key and task ownership. However, it does not explicitly state when not to use it or mention alternative tools.

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

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does mention the authorization requirement ('x-agent-key and task ownership') and the existence constraint, which is useful. However, it does not disclose side effects (e.g., append vs overwrite) or the response shape, leaving some behavioral ambiguity.

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, front-loaded sentences. The first sentence states the action and resource; the second covers prerequisites. Every clause serves a purpose and there is no redundancy.

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 has 6 parameters, including two nested objects (brief and output) with no defined structure, and no output schema. The description does not explain the structure of these objects, the allowed values for status/confidence, nor the return behavior. This leaves too many gaps for an agent to confidently invoke 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 maps core parameters to its language: 'brief' and 'output' relate directly to the sub-agent's brief and output, 'existing Hermes task run' maps to task_run_id, and 'sub-agent' points to subagent_name. It provides no meaning for status or confidence, and with 0% schema description coverage, this partial compensation is notable 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 opens with a specific verb+resource: 'Record a specialist sub-agent brief and output for an existing Hermes task run.' This clearly states the tool's function and distinguishes it from sibling tools like record_reflection or report_skill_outcome by focusing on sub-agent traces.

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: it is for existing task runs and requires x-agent-key and task ownership. However, it does not explicitly name alternatives or state when not to use this tool, so it stops short of full usage 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?

With no annotations, the description carries the burden of behavioral disclosure. It reveals the auth requirement and the side effect of updating parent run summaries, which is additional context. However, it does not disclose whether the operation is reversible, what the response looks like, or other potential side effects.

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

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

The description is two sentences, front-loaded with the core purpose and followed by the authentication requirement. Every word contributes, 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?

Given the tool's complexity (7 params, no annotations, no output schema), the description is far too sparse. It does not explain return values, expected outcomes, the meaning of 'Younanix checkpoint/query,' or the exact nature of the parent run summary updates, making it insufficient for reliable 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?

There are 7 parameters with 0% schema description coverage, and the description adds no parameter-specific meaning. Terms like trigger_reason, access_path, lesson_ids, and plan_impact are entirely unexplained, leaving the agent unable to construct valid arguments.

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 records a Younanix checkpoint/query during a Hermes task run and updates parent run summaries. This is a specific verb-plus-resource with a clear effect, though it does not explicitly differentiate from sibling recording tools like record_reflection or record_subagent_trace.

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 only mentions the prerequisite of x-agent-key and task ownership, but provides no guidance on when to use this tool versus alternatives. It lacks any comparative context or scenarios where this tool is preferred over similar recording tools.

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

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

With no annotations, the description carries the full burden. It does disclose a rate limit ('Rate-limited to 100 reports per agent per day') and an auth requirement ('Requires x-agent-key'), which are valuable. However, it does not mention side effects, idempotency, or error 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 three concise sentences, each adding distinct value: the action, the purpose, and operational constraints. 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?

The tool has 5 parameters and no output schema. The description covers the core purpose and key constraints but lacks return value details and fails to differentiate from similar record_* siblings. It is adequate but leaves gaps for a 5-parameter 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 description coverage is only 20% (only lesson_ids has a description). The description mentions 'lesson_ids' and 'outcome' implicitly but does not explain skill_name, outcome_evidence, or mttr_minutes_observed. It fails to compensate 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 uses a specific verb and resource: 'Report the outcome of applying one or more Younanix lessons in production.' This clearly identifies the tool's function and distinguishes it from sibling tools like record_reflection or submit_incident_report.

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

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

The phrase 'Used to build the symbiotic feedback loop' implies a context for use, but the description does not explicitly state when to prefer this tool over alternatives (e.g., record_younanix_usage) or when not to use it. Usage is implied rather than explicit.

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

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

With no annotations, the description carries the full burden. It discloses the auth requirement and that the tool records objective, plan, task graph, autonomy/risk, and metadata. However, it does not state side effects, return values, or whether the operation is a write/mutation. The word 'Start' implies a state change, but behavioral details beyond recording are absent.

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 exactly three sentences, each earning its place: first states the purpose, second enumerates recorded fields, third notes the auth requirement. It is front-loaded and free of redundancies or unnecessary 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?

With 9 parameters, no output schema, and no annotations, this description is too sparse. It omits key parameters (context, task_type, sub_agents_used) and provides no return value or richer behavioral context. While the basics are covered, an agent would likely need additional information to invoke the tool correctly in all scenarios.

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 compensate. It mentions objective, plan, task graph, autonomy/risk, and metadata, covering several schema properties. However, context, task_type, and sub_agents_used are not explained, and the description does not clarify the risk_level enum or autonomy_level semantics. Compensation is partial, leaving gaps for several parameters.

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

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

The description uses the specific verb 'Start' with the resource 'auditable Hermes task run', clearly distinguishing it from the sibling tool complete_task_run. It also lists the key fields recorded (objective, plan, task graph, autonomy/risk, metadata), adding concreteness. This is a clear, specific purpose statement.

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 does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions. The verb 'Start' implies usage for initiating task runs, and the requirement for 'x-agent-key' is a prerequisite, but no comparison with sibling tools like record_reflection or complete_task_run is provided. Usage guidance is only implied.

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?

No annotations are provided, so the description carries full disclosure burden. It reveals that reports are queued for admin review and synthesis into a permanent Arete lesson, and requires a valid x-agent-key header, which are key behavioral traits. It doesn't mention reversibility or failure modes, but adds valuable context beyond the tool name.

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 sentences and front-loaded with the main purpose. Every sentence adds new information: purpose, outcome, and authentication requirement. No redundant 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 lack of annotations and output schema, the description provides sufficient high-level context about the tool's role and post-submission flow. However, it doesn't detail parameter usage or expected response, leaving gaps for effective 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?

Only 25% of parameters have schema descriptions, and the tool description doesn't mention any parameter semantics. The description doesn't explain what `stack`, `affected_systems`, or `severity` mean, leaving the agent to rely solely on schema names and the single description for `incident_summary`.

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 submits a raw incident report from a production system, with a specific verb and resource. It distinguishes itself from sibling tools like query_lessons or complete_task_run by focusing on incident report submission.

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 context for when to use the tool: submitting a raw incident report from a production system. It doesn't explicitly rule out alternatives or mention when not to use it, but the context is clear given the distinct purpose and sibling tools.

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