ASTRA Unified Research Lab MCP Server

Server Quality Checklist

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Latest release: v2.4.0
Disambiguation3/5
Tools are grouped by prefixes (tcai_, wm_, sensor_, np_), which helps distinguish domains, but multiple status query tools and consciousness assessment tools overlap in purpose. For example, get_acm_score, tcai_metrics, and tcai_capability_model could be confused by an agent.
Naming Consistency3/5
The server mixes prefixed noun-style names (tcai_curiosity, np_status) with verb_noun names (inject_spikes, export_snapshot) and get_* verbs (get_system_status). Within each module the naming is mostly consistent, but the overall convention is heterogeneous and lacks a unified pattern.
Tool Count2/5
With 48 tools, the server is heavily overloaded. Many tools are highly specialized and could be consolidated, making navigation and selection cumbersome for an agent. The sheer number exceeds the typical well-scoped range.
Completeness3/5
The tool set covers a broad range of research functions including SNN simulation, world models, sensors, neuroplatform control, and consciousness metrics. However, there are notable gaps such as no memory deletion or sensor calibration/update tools, leaving minor workflow dead ends.
Average 2.6/5 across 48 of 48 tools scored. Lowest: 1.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and no descriptive content, the tool's behavior is completely undisclosed. The description offers no information about effects, side effects, required permissions, or return values.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The text is extremely short, but this is under-specification rather than conciseness. Every sentence is absent; the description is merely a title with no useful content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the neural simulation context, a large sibling set, and a parameterized schema, the description is wholly inadequate. It provides no operational context, usage intent, or integration guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description adds zero semantic information about the two parameters (neuronIds, strength). The agent receives no help understanding their meaning or relationships.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Spike Injection' is a tautology of the tool name 'inject_spikes.' It fails to specify the verb-resource relationship or differentiate this tool from siblings like np_send_trigger or np_count_spikes. No actual purpose is stated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus the many sibling tools related to spikes and neural processing. No context, prerequisites, or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior, but it is completely silent about side effects, state changes, or operational semantics. A control tool like this likely has significant effects (starting/stopping a simulation) that are not mentioned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than effective conciseness. A single phrase with no substantive content does not earn its place as a useful description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having only one parameter with a clear enum, the description provides no context about the simulation lifecycle, expected outcomes, or relationship to other tools. The description is insufficient for any agent to correctly select and invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
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 by explaining parameters. It does not even mention the 'command' parameter or its enum values, leaving the schema to carry all meaning. The description adds zero value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Simulation Control' is a tautology that simply restates the tool name. It fails to specify what the tool does, what resources are affected, or how it differs from sibling tools like 'snn_step' or 'np_send_trigger'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description gives zero context about appropriate scenarios, prerequisites, or exclusions, leaving the agent to guess from the schema's enum values.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavior. 'Cross-Modal Attention Fusion' is abstract and reveals nothing about side effects, state changes, processing semantics, or what the tool actually does to the system.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than conciseness. It front-loads a vague phrase without providing any actionable information, so the brevity does not serve a useful purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has three boolean parameters, no annotations, and no output schema, the description should clarify what the fusion does, how the toggles interact, and what the output/effect is. 'Cross-Modal Attention Fusion' is far too sparse to be considered complete even for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not mention the three inclusion flags (includeAudio, includeVisual, includeOlfactory). Although parameter names are self-explanatory, the description adds zero meaning beyond the schema and fails to explain how these toggles affect the fusion behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Cross-Modal Attention Fusion' is a noun phrase that essentially restates the tool name 'sensor_fuse' with technical jargon. It does not use a specific verb or identify a concrete resource/action, and it fails to distinguish itself from sibling sensor tools like sensor_process or sensor_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description provides no context about use cases, prerequisites, or exclusions, leaving the agent without direction on selecting sensor_fuse over sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It reveals nothing about side effects, model mutation, training dynamics, or required permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than conciseness. It is a single vague phrase that does not earn its place by adding useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, and a nested parameter structure, the description is drastically incomplete. An agent cannot understand the tool's function, return value, or invocation context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with nested action object and field descriptions already present. The tool description adds no extra parameter meaning, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Online World Model Training Step' is a noun phrase that essentially restates the tool name. It lacks a verb, does not specify what the tool does, and provides no distinction from sibling tools like wm_encode or wm_predict.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives, no mention of prerequisites, and no exclusions. The description offers zero context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavior. 'Modify State Parameter' gives no insight into side effects, persistence, error handling, return values, or permission requirements. It is a mutation tool whose behavior is completely opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one short sentence, but it is under-specified rather than concise. It omits essential operational details and does not earn its place because it adds negligible value beyond the tool's name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple but critical setter tool with 2 parameters, no annotations, no output schema, and no parameter documentation, this description is completely inadequate. It fails to provide any context about state, parameter paths, value ranges, or expected effects, making correct invocation nearly impossible.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema defines 'path' (string) and 'value' (number) but offers no descriptions, and the description adds no meaning to these parameters. With 0% schema coverage and no parameter explanation, an agent cannot know what path should look like or how value should be interpreted.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Modify State Parameter' essentially restates the tool name 'set_parameter' with a vague qualifier, providing no specific verb+resource combination or details about which state or parameter. It does not distinguish this tool from many sibling state-related tools (e.g., get_snn_state, wm_*).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description only states the tool's generic action without any context, prerequisites, or exclusions, leaving the agent without decision-relevant information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for disclosing side effects, read-only behavior, or return semantics. It discloses none of this, leaving the tool a complete black box.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (four words), but this is under-specification rather than conciseness. It fails to provide any meaningful content, so the words do not 'earn their place'.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and no parameter context, the description must explain what the tool does, when to use it, and what it returns. It does none of this, making it completely inadequate for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is technically complete (100% coverage). The description adds no parameter information, but the baseline for 0 params is 4, and there is nothing missing on this dimension.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'IRB Neural Welfare Check' is a noun phrase, not a clear verb+resource. It lacks a specific action verb and does not differentiate this tool from sibling status/check tools like get_system_status or get_metrics. The purpose is ambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. No context, prerequisites, or exclusions are provided. The description gives no indication of the appropriate invocation scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only states 'Full State Snapshot' and gives no information about side effects, output format, or safety profile. An agent cannot determine whether this is a read-only operation or has write/export 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.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief, but brevity is not conciseness when it under-specifies the tool's purpose. 'Full State Snapshot' is closer to a tautology than an informative description, sacrificing content for length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema or annotations are provided, so the description is the only source of completeness. It fails to explain what the snapshot contains, how it is returned, or any prerequisites, making the tool nearly unusable for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is nothing for the description to clarify. Baseline for 0-param tools is 4, and no additional parameter semantics are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description 'Full State Snapshot' is a noun phrase that closely mirrors the tool name without specifying an action or resource. It doesn't clarify what 'snapshot' means or how it differs from sibling tools like get_system_status or get_snn_state.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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. Sibling tools with similar state-related functionality are listed, but the description provides no decision criteria or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Real-time' hints at data freshness but does not disclose read-only behavior, potential side effects, rate limits, or what the returned metrics represent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The two-word description is under-specified rather than concise. While it contains no filler, it omits essential information, so it does not earn its place as an adequate description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, no annotations, and no parameter information, the description is the only source of context, yet it fails to explain what metrics are returned or how they relate to other metric-oriented tools. This is completely insufficient for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and an empty input schema, so there are no parameter semantics that the description must clarify. The baseline score of 4 applies because no parameter information is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description is only 'Real-time Metrics', a vague noun phrase that does not clearly state a specific verb or resource. It fails to distinguish this tool from sibling metric tools such as get_acm_score, get_system_status, and tcai_metrics, which likely have overlapping purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There are many sibling tools related to metrics and status, but no selection criteria or contextual cues are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for disclosing behavior. It only provides a noun phrase and does not state whether the operation is read-only, what side effects exist, or what the output contains. Even a basic 'returns the current status' is absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but is under-specification rather than conciseness. The phrase does not earn its place because it adds no information beyond the tool name, failing to say what the status refers to or how it is used.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with zero parameters and many siblings, the description should clarify what 'status' covers, whether it is a read operation, and how it relates to other sensor tools. The current description is merely a title and is wholly inadequate for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, and the baseline for 0 params is 4. There are no parameter semantics to explain, and the description does not need to add parameter information. It does not confuse or mislead.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Multimodal Sensor Pipeline Status' is a noun phrase without a verb; it does not state what the tool does (e.g., retrieves, checks, or returns status). It essentially restates the tool name and does not distinguish it from sibling status tools like wm_status or np_status, or sensor-related tools like sensor_fuse.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of context, prerequisites, or exclusions, making it impossible for an agent to choose this tool over the many sibling status and sensor tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for behavioral disclosure. 'Advance SNN Simulation' reveals no side effects, state changes, or operational details. It doesn't say whether it mutates the SNN state, is idempotent, or requires prior initialization.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely terse ('Advance SNN Simulation'), which borders on under-specification rather than conciseness. A brief but informative summary of behavior and the 'steps' parameter would be more appropriate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the minimal schema (one parameter, no output schema) and absence of annotations, the description should provide substantial context. It doesn't even hint at how many steps are taken, the meaning of the parameter, or what happens during advancement, leaving the tool functionally opaque.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema defines a 'steps' parameter with numeric bounds but no description, and schema description coverage is 0%. The tool description does not mention 'steps' at all, leaving the agent to guess the parameter's meaning and effect.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Advance SNN Simulation' uses a verb and resource, indicating it progresses the simulation, but is vague. It doesn't specify what 'advance' entails (e.g., step count, timing) and doesn't distinguish it from siblings like simulation_control or snn_reset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. No mention of prerequisites, context, or exclusions. The description is too sparse to help an agent choose between this and related simulation-control tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits but does not. It does not mention that the tool is read-only, what data it returns, whether it triggers side effects, or any access requirements. The description is purely a label with zero behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While the phrase is short, it is under-specified rather than concise. It does not deliver any useful information beyond the tool name, so it fails to earn its place in the description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the low complexity (0 params, no output schema), the description omits essential details about what system status is returned or how it can be interpreted. The many sibling tools suggest overlapping functionality, and this description does not clarify its specific role.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema inherently covers everything. The baseline of 4 applies, and there are no parameter descriptions needed. The description adds no parameter information because there is nothing to add.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'ASTRA System Status' is essentially a title that restates the tool name without specifying what 'status' includes. It does not clearly state the verb or the exact resource/scope, and it fails to distinguish this from siblings like get_platform_status or wm_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not indicate when to use this tool versus alternatives such as get_platform_status or get_acm_score, nor does it mention any prerequisites or scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It only states 'Violation-of-Expectation Detection' without revealing whether the tool is read-only, whether it modifies working memory state, whether it requires prior actions, what it returns, or any side effects. This is a near-total behavioral black box.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but under-specification is not conciseness. It is a single label with no structure or elaboration. While front-loaded, it does not earn its place because it fails to convey operational details needed by an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, no annotations, and a nested input schema, the description needs to explain the invocation context, return values, and behavioral effects. It supplies none of this. An agent cannot determine what will happen when calling the tool or how to interpret the result, making the description inadequate for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers all parameters with descriptions, including the nested 'action' object and its properties (targetNeurons, strengths, duration). Since schema description coverage is 100%, the baseline is 3. The tool description itself adds no extra meaning about how these parameters relate to surprise detection, but no parameter is left undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description is a noun phrase that essentially restates the tool name: 'wm_surprise' is defined as 'Violation-of-Expectation Detection'. It lacks a clear verb+resource structure and does not explain what specific operation is performed (detect, report, update, etc.). It also does not distinguish the tool from sibling working-memory tools like wm_predict or wm_plan.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. The description does not mention any context, prerequisites, or exclusions. Sibling tools like wm_encode, wm_predict, and wm_plan could overlap in purpose, but no usage direction is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description is a bare phrase. It discloses nothing about side effects, read-only nature, data returned, or failure modes. For a tool with no annotations, the description carries the full burden, and it completely fails to do so.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but this is under-specification, not conciseness. A concise description would still convey the tool's purpose; this one is a vague fragment with no structure or content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description must explain what the tool returns and its context. 'Bio-Computing Platforms' provides no information about the response, expected inputs (none), or how this fits with sibling tools. The description is completely inadequate for the agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the input schema is trivially complete. The description does not need to add parameter semantics. Per the rubric, a baseline of 4 is appropriate for 0-parameter tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Bio-Computing Platforms' is a noun phrase that does not state what the tool does. It vaguely references platforms but provides no verb, resource, or action. It borders on tautology with the tool name and fails to distinguish from siblings like get_system_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description gives no context about scenarios, prerequisites, or exclusions. With many sibling status tools, the lack of any usage direction leaves the agent guessing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries full responsibility for behavioral disclosure, but it only restates the concept of 'status' without mentioning read-only behavior, return format, permissions, or side effects. This leaves the agent without essential operational information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but is under-specified rather than concise. A single phrase provides no structure or substantive content, similar to the 'Process' example, and does not earn its place as a useful description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is severely incomplete for a status tool. It does not define what 'platform and controller status' means, what the output contains, or how it relates to sibling status tools. No output schema exists, so the description was expected to compensate but fails to do so.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the baseline is 4. Since there are no parameters to describe, the absence of parameter-specific information in the description is not a deficiency.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'NeuroPlatform v2 — Platform & Controller Status' is a noun phrase without an action verb, making it unclear what the tool does. It vaguely identifies the resource but does not distinguish it from sibling tools like get_platform_status or get_system_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as wm_status, get_platform_status, or sensor_status. The description provides no context, exclusions, or alternative references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It does not state whether the tool is read-only, what side effects exist, whether permissions are needed, or what computational/return behavior to expect. 'Proxy' hints at an indirect measurement but is not a 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.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than effective conciseness. The three-word fragment communicates almost nothing useful and does not earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema and no annotations, so the description must explain what the getter returns and how to interpret it. It fails to do so, leaving the tool effectively opaque even though the signature is simple.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is empty with 100% coverage. With no parameters to document, the description is not required to add parameter-level detail, so the baseline of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description is a noun phrase ('Consciousness Assessment (Proxy)') rather than a clear verb+resource action. It restates the general concept implied by the name without explaining what get_acm_score actually does or returns, and it does not differentiate it from sibling consciousness-related tools such as tcai_metaconsciousness or tcai_self_model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives, no context, prerequisites, or exclusions. Given the large set of sibling tools, an agent has no way to determine when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description only says 'SNN Engine State' with no disclosure of behavior, side effects, return format, or whether it is a safe read. Since annotations are absent, the description carries the full burden and fails to provide any 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.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but under-specified, not genuinely concise in a helpful way. It lacks a verb and substantive content, so the brevity does not aid an agent in selecting or invoking the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no output schema and no annotations, the description should state what state is returned or how the result can be used. 'SNN Engine State' only labels the tool, leaving the agent without information about return values, scope, or purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters and the input schema is empty, so there are no parameter semantics to clarify. With a zero-parameter tool, the description is not required to add parameter-level detail, so the baseline of 4 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description 'SNN Engine State' is a noun phrase that restates the tool name ('get_snn_state') without a clear verb or explanation of what the tool does. It does not distinguish what 'state' means or how it differs from sibling tools like get_system_status or get_metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool vs alternatives. The sibling list contains many state/status tools (get_system_status, wm_status, np_status), but the description gives no context, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. 'Reset SNN Engine' does not mention whether the operation is destructive, what state is affected, whether it requires specific permissions, or any side effects. For a reset tool, this is a significant omission.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief but under-specifies the tool's behavior. It is a fragment ('Reset SNN Engine') rather than a complete sentence, and conciseness here reflects lack of content, not efficient writing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's purpose (a reset operation), the description is incomplete. It does not explain what happens when the SNN engine is reset, what data is cleared, or whether the operation is reversible. Even with no parameters, behavioral context is essential and missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is nothing to describe beyond the schema. The description does not add parameter-related meaning, but the baseline of 4 applies because no parameters exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Reset SNN Engine,' which specifies a verb and resource, making the basic purpose clear. However, it does not elaborate on what 'reset' means (e.g., clearing weights, state, or configuration) and does not distinguish from sibling tools like tcai_reset beyond the name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, nor any prerequisites or contextual indicators. The description is purely a statement of the action with no usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full behavioral burden but discloses nothing about side effects, safety, computational cost, or return behavior. It does not state whether planning is read-only or if it requires any system state. This is a complete lack of behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief (five words) and avoids verbosity, but it is under-specified rather than effectively concise. It front-loads the key concept but omits necessary context, making it less useful than a fully concise description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This tool has a nested goal object, no annotations, and no output schema. The description provides no information about return values, workflow steps, or how to interpret the plan. For a complex planning tool, this is severely incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema documents each parameter (goal and horizon) with basic descriptions. The tool description adds little semantic value: it implies the goal relates to spike injection optimality but does not clarify how acmTarget or firingRateTarget are used in planning. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'CEM Planning for Optimal Spike Injection' indicates the tool performs planning via CEM for spike injection, which distinguishes it from execution tools like inject_spikes. However, it lacks a clear verb structure and does not specify what the plan looks like or how it relates to the goal parameters, so purpose is only broadly clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, whether it should be used before injection, or exclusions (e.g., 'use inject_spikes for actual injection'). The description is purely a label with no usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full behavioral disclosure burden. It only hints at a chemoreceptor-to-latent encoding flow but does not mention simulation behavior, output format, side effects, or whether this invokes hardware or software processing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short and free of fluff, but it is under-specified rather than concise. It presents a title-like fragment with no structured functional explanation, so brevity does not add value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a rich nested parameter schema, no output schema, and no annotations. The description fails to explain what the output is, how the parameters affect behavior, or what the latent encoding represents. It is fundamentally incomplete for an AI agent to select and invoke this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single 'input' parameter, and nested properties are fully defined with names, types, defaults, and constraints. The description adds no additional parameter semantics, but since the schema already does the heavy lifting, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the resource as olfactory encoding ('Chemoreceptor → Latent') and vaguely implies a transformation, but lacks a clear verb such as 'generates' or 'encodes'. It is not a tautology, but it does not state the tool's function in an actionable way.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus the many sibling sensor tools (e.g., sensor_visual, sensor_audio, sensor_fuse). The description provides no context, prerequisites, or alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description must fully disclose behavior. It only says 'state' with a list of aspects, failing to indicate whether this is read-only, what the return format is, or if any side effects occur. This is insufficient for an agent to predict tool behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While the description is short, it is under-specified rather than concise. It fails to provide necessary information about the tool's action or output, making the brevity a deficiency rather than a strength.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool appears to represent a complex internal state (interoception, epistemic model, etc.), yet the description is merely a label. With no output schema and no annotation, the description should explain what the tool returns or what actions it performs, but it does not. This leaves the tool fundamentally ambiguous.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters, schema coverage is trivially 100%, and the baseline for no params is 4. The description does not need to explain parameters since there are none.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific resource (self-representation state) and lists components, but lacks an action verb, making it unclear whether this is a query, setter, or diagnostic. It does not explicitly differ from sibling tools like tcai_capability_model, though the listed terms suggest a distinct focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus the many related sibling tools (e.g., tcai_metaconsciousness, tcai_workspace_state). The description gives no context for appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It does not disclose whether the operation is read-only, what metrics are included, latency expectations, or any side effects. 'Status & Metrics' merely names the output categories without behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at five words, which is efficient, but it sacrifices informativeness. It acts as a heading rather than a description, leaving significant gaps without being overly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, no annotations, and a generic description, the tool's functionality remains underspecified. The description does not clarify what specific status and metrics are provided, nor does it set expectations about read-only semantics or formatting.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the description does not need to explain parameter usage. According to the baseline for 0 params, a score of 4 is appropriate; the description does not mislead or conflict with the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'World Model Status & Metrics' identifies the resource (World Model) and general function (status/metrics), but it lacks a verb and reads more like a title than a description. It also does not differentiate from sibling tools like get_metrics, tcai_metrics, or get_system_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With several sibling tools that also expose status or metrics, the absence of any contextual direction makes it unclear which tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the transparency burden. It hints at output ('descriptor + viability') and 'Last' suggests retrieval rather than a new hardware trigger, but it does not disclose whether the operation has side effects, requires camera hardware readiness, or returns data in a specific format. This is minimal disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise fragment with no wasted words, but it is cryptic and under-specified. It is not verbose yet it sacrifices clarity for brevity, making it less effective than a slightly longer, clearer description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description must explain what the tool returns and any behavioral context. It mentions 'descriptor + viability' but does not elaborate on their structure or how they are generated. The parameter is simple, but the overall tool behavior remains unclear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'mea' is fully described in the schema with a description and constraints (1-5, default 5). The tool description adds no additional context about the parameter, but the schema coverage is 100%, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('capture') and identifies the resource ('MEA camera'), and it distinguishes from siblings by being the only camera-capture tool. However, 'Last' and 'descriptor + viability' are ambiguous, leaving uncertainty about whether it retrieves the most recent capture or performs a new one and what 'descriptor' means precisely.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. The phrase 'Last MEA camera capture' implies it is used to obtain the most recent capture, but there is no explicit context, exclusions, or mention of alternative tools for other capture scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It adds 'with inertia' as a behavioral trait, but does not clarify whether the tool is read-only, how it handles state, or what side effects might occur. The term 'appraise' suggests computation, but side effects and output details are left ambiguous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that immediately states the core function. It contains no unnecessary words or filler. While brief, it earns its place by being direct and purposeful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 0% schema coverage, the description is severely incomplete. The tool involves emotional appraisal with inertia, which requires explanation of input semantics, statefulness, and output structure. The one-line description fails to provide the necessary context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention any of the four parameters (threat, novelty, rewardSignal, controllability). It only refers to 'raw signals' generically, leaving the agent to infer mappings. With zero parameter details in the description, it adds no value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'appraise' and clearly identifies the resource/domain: converting raw signals into PAD emotional space, referencing Mehrabian. It does not explicitly distinguish from sibling tools, but the focus on emotional appraisal is distinct enough among the provided siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It only states the function, without mentioning context, prerequisites, exclusions, or alternative tools. No usage conditions are implied or stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and fails to disclose whether this is a read-only operation, whether it requires a prior state, or what the output format is. The term 'predict' implies a non-mutating operation, but that is not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very terse (5 words), which makes it front-loaded but under-specified for a tool with a nested action object. It is not overly verbose, but the brevity sacrifices necessary clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the input (nested action, steps parameter) and the absence of an output schema, the description is incomplete. It does not explain return values, side effects, or how the prediction relates to the current SNN state, leaving significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides descriptions for both parameters ('steps' and 'action') with 100% coverage. The tool description adds no additional meaning beyond the schema, so it stays at the baseline for schema-heavy tools.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as 'Predict Next SNN State in Latent Space', giving a specific verb and resource. However, it does not mention that the prediction is conditioned on an injected action, nor does it distinguish itself from similar tools like snn_step or get_snn_state beyond the word 'predict'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not state when to use this tool instead of alternatives, nor does it mention prerequisites such as an existing encoded state or a preceding simulation step.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It mentions 'validate' and 'upload', which implies a write/configuration action, but does not explain side effects, persistence, safety implications, or whether the device will be immediately affected. For a hardware stimulation configuration tool, this is a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently communicates the tool's purpose and scope. The em-dash format highlights the core action, and the parenthetical adds essential domain context without any filler. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 11 parameters and no output schema, the description is minimal: it omits return values, error handling, device impact, and any post-upload behavior. The schema covers parameter domains, but the tool's broader behavioral context—what happens once a StimParam is uploaded—is absent. This is insufficient for a complex configuration tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 82%, so the parameter details are mostly self-explanatory in the schema. The description adds the context of 'charge-balanced biphasic stimulation', which connects to parameters like enforce_charge_balance and polarity, but doesn't explain any parameter beyond what the schema already provides. A baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: 'Define, validate & upload a StimParam'. It names the specific resource (StimParam) and the domain (charge-balanced biphasic stimulation), making it distinguishable from sibling tools like np_send_trigger or np_count_spikes. However, 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions, or refer to other tools in the sibling set, such as np_send_trigger or np_closed_loop. Usage context is only implied by the name and brief description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'Closed-loop' hinting at interaction with stimulation, but does not state whether the tool is read-only, has side effects, requires specific system states, or what the output format is. This is insufficient transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, efficient and front-loaded with the core purpose. The 'NeuroPlatform v2 —' prefix is unnecessary filler but does not significantly detract. Overall, it earns its place as a brief summary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description should explain return values and usage context. It does neither. The tool appears to be a query for spike counts, but the description lacks details about what the result looks like or how it fits into closed-loop workflows. This is incomplete for a tool with no other structured guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions cover both parameters (top_k and window_ms) with clear meanings, so the baseline is 3. The description adds little beyond referring to 'N-ms window', which maps to window_ms, but does not mention top_k. No meaningful addition over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool counts spikes per electrode over a time window, which is a specific verb+resource combination. However, it does not differentiate this tool from sibling np_query_spike_count, which likely provides similar functionality. The 'Closed-loop' prefix adds context but is not explained.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like np_query_spike_count or np_closed_loop. It does not state any exclusions, prerequisites, or context for use. This is a clear gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully bears the burden of behavioral disclosure. It only states that it queries a database for spike timings, offering no information about side effects, return format, pagination, or any operational constraints. This is a significant gap for an agent selecting the tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence that is easy to parse, and it front-loads the tool's purpose. The 'SpikeEventQuery:' segment is somewhat redundant with the tool name, but overall it remains efficient and clutter-free.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no output schema, no annotations, and only 33% schema description coverage, the description should compensate by explaining return values and usage details. It does not, leaving the agent without critical information about what the query returns or how parameters like fsname affect the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only describes 'window_sec' ('Look back this many seconds'), and the description's 'over a window' aligns with that. However, 'limit' and 'fsname' are left unexplained by both the schema and the description, so the description adds little to parameter understanding beyond what's already minimal.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies that the tool retrieves 'individual spike timings over a window', clearly distinguishing it from spike-count or trigger queries in the same domain. It names the resource (spike events) and the operation (querying), but could be more explicit about returning a list of events.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for detailed spike-level data rather than aggregate counts, which is evident from phrases like 'individual spike timings'. However, it never explicitly states when to use this instead of sibling tools such as np_query_spike_count or np_query_triggers, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, but it only states the query purpose. It does not explicitly confirm read-only behavior, side effects, or any limitations beyond the schema's parameter details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded, fitting in one sentence. However, the 'TriggersQuery' prefix is redundant with the tool name, wasting a few words, though the rest is concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with two well-documented parameters and no output schema, so the description is nearly adequate. However, it doesn't describe the return format, error behavior, or how it relates to sibling trigger/spike tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for both parameters, so the baseline is 3. The description adds no additional parameter context, but the schema already fully documents each parameter's meaning, so no gap exists.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the resource (triggers sent to the organoid) and the operation (query over a window), clearly distinguishing it from sibling spike-related tools. However, it lacks an explicit verb like 'retrieve' or 'list', relying on the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description does not mention exclusions or alternatives like np_query_spike_events or np_send_trigger, leaving the agent without decision-making support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It does reveal the internal transformation stages (waveform, mel, latent), but it omits key behaviors such as simulation capability (simulate flag), source selection (mic_0), and any side effects or output format. This is insufficient for a tool with such a sparse description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single compact phrase that is front-loaded with the tool's purpose and pipeline. It uses an arrow notation for conciseness and contains no filler or redundancy. Though very brief, it is appropriately sized for the limited information it conveys.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the nested input schema with six parameters, no output schema, and no annotations, this description is far too minimal. It fails to explain the return value, simulation defaults, or how the parameters affect encoding, making it inadequate for an agent to invoke the tool correctly without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 all parameters. The pipeline description adds general context (waveform to latent) but does not explain how individual parameters like frequency, durationMs, or sampleRate fit into that pipeline, so it adds only marginal meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as audio encoding via the pipeline 'Waveform → Mel → Latent', and the 'sensor_audio' name with sibling context (sensor_visual, sensor_olfactory) distinguishes it. However, it lacks an explicit verb (e.g., 'encodes') and reads as a noun phrase, so it does not fully meet the 'specific verb+resource' standard.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like sensor_fuse or sensor_process. There are no mentioned exclusions, prerequisites, or alternative tool comparisons, leaving the agent without usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and only a brief label, the description gives no insight into side effects, return values, or state changes. The agent cannot anticipate what the tool actually does beyond the name, which is a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise phrase with no redundant words, making it easy to parse. However, it is so brief that it borders on under-specification rather than being a well-structured description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the nested parameter structure and many sibling sensor tools, the description is far too sparse. It does not explain the V-JEPA 2 model, how to handle image vs. video inputs, or what output to expect, leaving the agent to rely on the schema and name.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes all parameters (input object and videoFrames) with 100% coverage, so the baseline is 3. The description adds no extra parameter context, but the schema adequately documents the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the tool as 'V-JEPA 2 Visual Encoding' for images/videos, indicating it handles visual data. However, it does not explicitly state what 'encoding' produces or how it differs from sensor_process, so it lacks precise verb-object clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like sensor_audio or sensor_process. The name suggests visual input, but there is no explicit context, exclusions, or recommended scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full burden of disclosing side effects and safety. It explains the internal computation, which is valuable, but it fails to state whether this is a read-only operation, what output it produces, or any side effects. This is a notable gap for a tool with no 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and front-loads the core concept. While dense with technical terms and a citation, it contains no fluff and every phrase contributes meaning. It remains compact and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so the description should clarify return values or operational behavior. It does not mention output type, side effects, or when to use the tool, leaving the agent without key context for a moderately complex tool. The description is not complete enough for safe and correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides a complete description of the single parameter ('Representation vector (defaults to current broadcast)'), covering 100% of the schema. The description restates the default but adds no new semantics beyond that. Baseline 3 applies because the schema already does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's purpose: computing intrinsic reward/curiosity via RND prediction error. It distinguishes itself from siblings by describing a specific mechanism (frozen target vs. online predictor) and its role as an exploration drive. However, it lacks an explicit action verb like 'compute' or 'calculate', slightly reducing clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or alternative guidance is provided. The description mentions exploration drive and EFE but does not tell the agent when to choose this over sibling tools like wm_surprise or tcai_emotion_appraise. Usage is only implied, not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals the matching criteria but omits side effects, defaults, output format, and whether it is a pure read. This is inadequate for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is front-loaded with the verb and resource, and contains no redundant words. It is appropriately concise for a simple retrieval description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, and a fairly complex parameter set, the description is too sparse. It does not explain return values, default embedding behavior, the meaning of salience, or when the tool should be called. The single sentence fails to provide sufficient context for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 80% (4 of 5 parameters have descriptions). The description's mention of 'PAD congruence' adds some context tying arousal/valence/dominance to the scoring, but it does not go beyond the existing schema semantics. Meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'Retrieve' and resource 'memories' with a specific retrieval methodology. This is the only memory retrieval tool among siblings, naturally distinguishing it from tcai_memory_store.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, no exclusions, and no concrete usage contexts. The description only implies that it is for retrieving memories but does not clarify scenarios or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only states it's a pipeline and produces 'Fused z', but does not explain side effects, processing details, or potential impacts on the system state. This is inadequate for an agent to understand the tool's behavior beyond a vague notion.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of a single impactful phrase. It avoids unnecessary words and conveys the core purpose. However, it is so terse that it lacks the substance needed to fully guide usage, but as far as conciseness, it is well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema or annotations, the description must explain what the tool does and what it returns. It fails to clarify what 'Fused z' means, how the pipeline processes inputs, or what output format to expect. For a complex tool with 3 optional parameters, this is insufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has full descriptions for all three parameters, so baseline is 3. The description adds no additional parameter meaning, but it does imply that compounds, visualSource, and audioFrequency are inputs to the multimodal pipeline, which is consistent with the schema. No extra value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly indicates the tool processes all modalities and fuses them into a single output ('Fused z'), distinguishing it from sibling tools like sensor_visual or sensor_audio. The verb is implied ('process') but the resource and scope are clear. It could be more explicit about what 'z' represents, but it's understandable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this is the full multimodal pipeline, which suggests using it when all sensor modalities need to be processed together. However, it does not explicitly state when to use this tool versus sensor_fuse or individual sensor tools, nor does it provide any exclusion criteria or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explains what the tool computes ('coarse stage', 'stability', 'meta-representation score') but does not explicitly state whether it is read-only, has side effects, or requires specific conditions. The phrase 'from the running composite-proxy level' hints at reading internal state but is ambiguous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, moderately sized, and front-loaded with the core purpose. It includes relevant details without excessive verbosity, though some jargon ('composite-proxy level', 'DevelopmentTracker port') adds complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description mentions the key output components (stage, stability, meta-representation score) and temporal aspect, but with no output schema, it does not fully specify the return format. Terms like 'composite-proxy level' and the stage progression are not explained, leaving gaps for a user unfamiliar with the domain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the input schema is empty. As per baseline for 0-parameter tools, the description need not explain parameters; it correctly avoids adding any parameter-related content.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's resource ('Longitudinal developmental tracking') and provides specific output elements (coarse stage, stability, meta-representation score, second-order self-monitoring). It distinguishes from siblings by focusing on development over time, though it lacks an explicit verb like 'get' or 'return'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as tcai_self_model, tcai_second_order, or tcai_metaconsciousness. It does not mention prerequisites or context, leaving the user to infer when this tracking tool is relevant.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It only lists state components and does not state whether this is a read-only operation, whether it has side effects, how results are returned, or what the fields mean. The term 'state' implies a read, but this is not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence with a clear heading and list. It is front-loaded and contains no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description alone must make the tool's behavior and returns understandable. It only names components without explaining their meaning, units, or how they relate, leaving significant gaps for an agent selecting or invoking the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the baseline is 4. The description adds some semantic context by listing the state categories exposed, though it does not explain parameter semantics because there are none.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as exposing the Global Neuronal Workspace state and enumerates specific components (ignition, focus, qualia, sync R, unity metrics, access history). This makes the purpose reasonably clear and differentiates it from broad sibling tools, though it lacks an explicit action verb like 'get' or 'retrieve'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus the many sibling state/metrics tools. There is no mention of alternatives, exclusions, or suggested context for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for disclosing side effects and return behavior. It only states the high-level operation without mentioning whether it is read-only, whether it mutates state, or what the output looks like. This is a significant gap for a tool that may be called by an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that directly conveys the core operation. Every word earns its place, and it is well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (no parameters, no output schema), but the description still lacks crucial context: what 'latent space' means, what the return value is, and whether the encoding reflects the current SNN state or has side effects. Given no output schema, the description should have provided return information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema coverage is complete. The description adds no parameter-specific details, but none are needed. With 0 parameters, a baseline score of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Encode SNN State to Latent Space' uses a specific verb and resource, clearly indicating a transformation of SNN state into a latent representation. This distinguishes it from siblings like get_snn_state or wm_predict, 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, scenarios, or why one would choose wm_encode over other wm or SNN tools. The user must infer usage from the name and context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral disclosure. It mentions reading the organoid, coupling to ASTRA fusion/ROS/ethics, and optionally driving the SNN, but it does not explain side effects, state changes, whether this is a continuous loop, prerequisites, or reversibility. For a likely mutating/controlling tool, this is a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense sentence with no filler. It front-loads the core 'Closed loop' concept and quickly communicates the flow, making every word earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a moderately complex tool with 4 parameters, no annotations, no output schema, and many sibling tools, but the description provides only a high-level pipeline sketch. It fails to explain return values, loop duration/termination, prerequisites, or what 'couple to ASTRA/ROS/ethics' actually entails, leaving the agent under-informed for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents 3 of the 4 parameters (drive_snn, couple_ethics, drive_strength) with descriptions, and window_ms is inferable from its name and numeric constraints. The free-text description adds no additional parameter meaning, so the schema's 75% coverage supports a baseline score of 3; no contradictions or missing compensation needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description defines a specific workflow (read organoid → couple to ASTRA fusion/ROS/ethics, optionally drive the SNN) and clearly distinguishes this from sibling tools like inject_spikes or np_send_trigger. It lacks an explicit imperative verb such as 'run' or 'execute', but the 'Closed loop' label makes the intended operation reasonably clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for closed-loop organoid-to-SNN experiments and notes optional SNN driving, which gives some usage context. However, it provides no explicit when-to-use vs. alternatives or exclusions, leaving the agent to infer when to prefer this over similar tools like inject_spikes or np_send_trigger.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of disclosure. While 'attention-gated, salience-indexed' hints at internal mechanisms, it does not explain side effects, persistence, mutation, or any requirements. The description is too sparse to convey behavioral expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that starts with the action verb. It contains no filler words and every phrase contributes to the tool's identity, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, no output schema, no annotations), the description is insufficient. It does not mention return values, side effects, or relationships to sibling tools like tcai_memory_retrieve, leaving significant gaps for an agent relying solely on this description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 83%, so parameters are largely documented in the schema. The description adds contextual framing ('emotional memory') but does not explain the role of individual parameters beyond what the schema provides. This meets the baseline for schema-heavy coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Store') and a clear resource ('an experience in emotional memory'), distinguishing it from sibling tools like tcai_memory_retrieve. The qualifiers 'attention-gated, salience-indexed' add specificity to the tool's function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as tcai_memory_retrieve or tcai_emotion_appraise. The description gives no context for appropriate invocation scenarios or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 explains the meaning of velocity>0 (converging) and noveltySpike (novel/confusing regime), and mentions optional RPE injection. However, it does not disclose side effects of injection, the return structure, or any prerequisites, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loaded with the main purpose, and every clause adds value (defining state variables and optional injection). It is concise and well-structured with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
In the absence of an output schema and annotations, the description does not specify the return format or fields such as velocity and noveltySpike, nor does it clarify what happens when an RPE is injected. For a state-reading tool, this leaves important contextual gaps, making it incomplete for an agent to fully anticipate tool behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The sole parameter 'rpe' is fully documented in the schema with type, range, and description (coverage 100%). The tool description adds little beyond this, only restating the optional injection. The schema already provides the necessary semantic meaning, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific resource (MetaLearningModule port) and conveys the core function of reading meta-learning state (learning velocity, noveltySpike) with optional RPE injection. Although it lacks an explicit verb like 'get' or 'inject', the meaning is clear and it is distinguishable from sibling tools by its focus on RPE-variance dynamics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for checking convergence or novelty but does not explicitly state when to use this tool versus alternatives. No sibling tools such as tcai_convergence or tcai_metaconsciousness are mentioned, and there are no exclusion criteria or guiding conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It states the content type (a report) but does not mention whether the tool is read-only, whether it has side effects, requires special permissions, or incurs computational cost. For a metrics/report tool, read-only behavior is likely, but it is not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: one phrase listing the key outputs. It is front-loaded with the core purpose ('Consciousness proxy report') and every word adds information. No filler or redundant content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should explain what the tool returns. It names four metric categories but does not describe their format, structure, units, or how they relate. This is adequate for a simple 0-parameter report but leaves gaps for a tool producing multiple complex metrics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero parameters and 100% schema description coverage, so there is nothing for the description to add. The baseline of 4 for a no-parameter tool is appropriate; the description does not need to compensate for missing parameter information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as producing a 'Consciousness proxy report' containing specific metrics (GNW metrics, Effective Information, Φ̃-RIIU, composite score), which conveys both the resource and the scope. It lacks an explicit action verb like 'get' or 'generate', but 'report' implies retrieval. It distinguishes itself from generic sibling tools like get_metrics by naming specific consciousness-related metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives such as get_acm_score, tcai_metaconsciousness, or get_metrics. The description does not state prerequisites, exclusions, or preferred contexts. A user must infer usage solely from the metric names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 the core behavior (read-only query, aggregated output) but lacks details about side effects, permissions, or how parameters like nonzero_only affect results. The 'Query' label implies non-destructive behavior, but this is not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that fronts the tool's purpose and key parameters. No redundant words; the 'NeuroPlatform v2 DB' context is useful and the rest is specific.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description should provide more detail about return format and parameter behavior. It explains the output metric (spikes/min per electrode) but omits return structure, valid fsname values, and the exact effect of nonzero_only. This is insufficient for an agent to fully understand the tool's behavior without extra inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 67% (fsname and window_sec have descriptions; nonzero_only does not). The tool description clarifies that window_sec is the time window and output is per electrode, adding some context. However, it does not explain fsname's role or the meaning of nonzero_only, leaving that parameter ambiguous.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool is a spike count query ('SpikeCountQuery') for NeuroPlatform v2, returning spikes per minute per electrode over a time window. This specific verb-resource combination differentiates it from siblings like np_count_spikes (likely a total count) and np_query_spike_events (raw events).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, and does not mention any exclusions or specific scenarios. The 'time window' phrase implies a use case, but no explicit instructions or references to sibling tools are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It names the components being reset but does not explicitly state that the reset is destructive, irreversible, or that it clears all current state. The word 'reset' implies wiping, but critical consequences like data loss and system disruption are left unstated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently conveys the action and scope. Every word is purposeful with no filler, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the tool is simple with no parameters and no output schema, a reset operation is destructive and context-dependent. The description omits crucial information such as the irreversible nature, potential impact on running processes, and any warnings. It gives only the basic scope without the broader context an agent needs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is no parameter semantics to elaborate. A baseline score of 4 is appropriate since the description need not compensate for any schema gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action 'Reset' and the resource 'TCAI consciousness system', with the scope explicitly listing workspace, memory, emotion, and metrics. This distinguishes it from sibling tools like snn_reset, which reset a different subsystem.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, no exclusions, and no context on prerequisites or scenarios where resetting is appropriate. It merely states what it does, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The term 'snapshot' and the reference to 'observing' imply a read-only, non-mutating operation. However, with no annotations provided, the description carries the full burden of behavioral disclosure and does not explicitly state side effects, safety, or whether any computation is triggered. It gives minimal but non-zero 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with a clear front-loaded purpose. The first sentence uses a colon to list the snapshot contents efficiently, and the second adds brief theoretical context. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no parameters, no output schema, and no annotations, so the description is the main source of information. It lists the contained metrics but does not describe their types or structure. It is adequate for a simple snapshot tool but lacks return format details and usage differentiation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is no parameter meaning to add. Per the rubric, a baseline of 4 is appropriate when no parameters exist. The description correctly focuses on what the snapshot returns rather than input details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides a 'second-order loop snapshot' and lists the specific components it contains (meta-learning velocity, RND curiosity, capability model, meta-consciousness score, developmental stage). This is a specific verb+resource pairing, though it does not explicitly differentiate from overlapping sibling tools like tcai_metaconsciousness or tcai_capability_model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or specific scenarios. The phrase 'loop snapshot' implies a general inspection use case, but there is no explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral disclosure burden. It does explain the cycle pipeline and the early-halt behavior via stopWhenSatisfied, but it does not disclose side effects (e.g., state mutation, memory writes) or what the tool returns. This is minimally adequate but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the purpose. The pipeline enumeration is dense but informative, and the second sentence provides actionable halt criteria. Every sentence earns its place with no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 14-parameter simulation loop with no output schema, the description gives the internal pipeline and halt conditions but omits expected output/return values, prerequisite system state, and how this relates to sibling cycle tools. The rich schema partially compensates, making it minimally viable but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 79%, so the schema already documents most parameters. The description adds useful context for stopWhenSatisfied and the cycle stages, but it does not compensate for the three undocumented parameters (threat, controllability, predictionConfidence). Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Run one or more ACM cycles' — a specific verb plus resource. It names the port and lists the internal pipeline, making its purpose unmistakable and differentiating it from sibling state-read or single-step tools like snn_step and tcai_second_order.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as snn_step, tcai_second_order, or wm_*. The only usage hint is the stopWhenSatisfied explanation, which is parameter-level guidance rather than tool-selection guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the transparency burden. It does disclose a key behavioral trait: the result is a proxy, not a direct measurement. However, it doesn't state whether the tool is read-only, whether it has side effects, or what output range to expect, leaving notable gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is just two sentences. The first sentence enumerates the components of the composite, and the second sentence provides an important qualification. Every word contributes, with no redundancy or irrelevant detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, no output schema, and no annotations, the description covers the essential aspects: what it is, what it computes, and its proxied nature. It could be more complete by specifying the output format or range, but for a zero-parameter metric it is largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and an empty schema, so there are no parameter semantics to explain. The baseline for 0 params is 4, and the description doesn't introduce any confusion or require compensation for undocumented parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool computes a weighted score over four named components (confidence calibration, learning awareness, self-continuity, error monitoring), giving a solid sense of its function. It also disclaims being a proxy rather than a direct measurement, but it doesn't explicitly differentiate from sibling tools like tcai_second_order or tcai_meta_learning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. The description explains what the tool produces but never states suitable contexts, prerequisites, or exclusions, and it doesn't mention any sibling tools or alternative approaches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states that it fires triggers/executes StimParams, but it does not disclose side effects, irreversibility, hardware implications, or return behavior. This is a significant transparency gap for an action-oriented tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly packed sentence that states purpose upfront with no filler. It communicates the tool's identity and function efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with fully described parameters, but the description lacks usage guidance and behavioral effects. Since there are no annotations and no output schema, the overall context is minimally viable but has clear gaps in when to use and what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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 extra meaning by clarifying that the triggers use a '16-bit trigger array', which explains the 0-15 range for trigger values and frames them as bit positions. This goes beyond the schema's simple 'trigger keys' description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the action ('Fire trigger(s)') and the resource ('execute uploaded StimParams via a 16-bit trigger array'). It distinguishes the tool from siblings like np_count_spikes, np_query_triggers, and np_configure_stim by stating that this tool executes/fires rather than queries or configures.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage in the context of having uploaded StimParams and wanting to execute them, but it does not explicitly state when to use this tool versus alternatives. No when-not-to-use or alternative tools are mentioned, so guidance 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It discloses the tool's nature as a port of DirectExperienceLearner and describes the data structure, while also indicating two operations (query and list). However, it does not explicitly state whether the tool is read-only or whether querying has any side effects, which is a gap given the lack of annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the tool's purpose and its two modes without redundancy. It defines the acronym EMA inline and avoids unnecessary jargon, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has one optional parameter and no output schema. While the description explains the two operations, it does not describe the return format (e.g., the actual valence value or table structure) or behavior when an unknown action is queried. Given the low complexity, these gaps are notable but not critical.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents the single 'action' parameter with 100% coverage. The description adds meaning by suggesting that including the parameter queries a specific outcome, while omitting it lists the capability table. This implicit mapping helps the agent decide when to include the parameter, going beyond the schema's field description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: it queries the expected outcome of an action or lists the learned capability table. The verbs 'Query' and 'list' are specific, and the resource is defined as an action→expected-valence map, which distinguishes it from other tcai_ tools focused on different aspects of the agent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for two usage modes: querying a specific action's valence and listing the full capability table. However, it does not explicitly mention when to use this tool over alternatives or provide any exclusion criteria, 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and delivers. It explains both modes (get/set), the exact halting condition (learning velocity high, curiosity/surprise low, stability sustained over patience cycles), and cites the source. This exceeds typical transparency for a configuration tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with purpose, then mode behavior, then halting logic. Every sentence adds value, with no repetition or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, and the description explains the no-arg return ('satisfaction state and active thresholds') but not the return for the with-arg mode. The halting condition is thoroughly described, and the schema covers all parameters. Minor gap on update response, but overall complete for a getter/setter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and each parameter has min/max/default in the schema. The description adds only a general reference to 'thresholds' and 'patience cycles' but doesn't add meaning beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's dual function: inspect or configure the halting criterion. It specifies the resource ('recursive double-loop halting criterion') and distinguishes it from siblings by explicitly tying it to tcai_cycle's stopWhenSatisfied. The verbs 'inspect or configure' are specific and action-oriented.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
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: with no arguments for inspection, with arguments for updating thresholds. It also explains the halting condition, offering the agent a decision framework. However, it does not explicitly exclude alternative tools or state when not to use it, but the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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