ASTRA MCP Server

Server Quality Checklist

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Latest release: v2.7.0
Disambiguation4/5
Tools are well-differentiated with clear prefixes (np_, tcai_, sensor_, wm_) that group related functions. Some potential overlap exists between get_acm_score and tcai_metrics, and between check_ethics and np_closed_loop, but descriptions clarify distinct purposes.
Naming Consistency4/5
Naming is consistent within subgroups (e.g., all np_* follow noun_verb, all tcai_* are descriptive). However, across the whole set there is a mix of verb_noun (snap_reset), noun_verb (sensor_audio), and no clear single convention.
Tool Count3/5
50 tools is high for a single server, though the scope (neuroplatform, consciousness, world model, sensors) is broad. The count is borderline; many tools could be merged or modularized.
Completeness4/5
The tool set covers monitoring, control, simulation, learning, memory, and sensory processing comprehensively. Minor gaps exist, such as no tool for deleting memories or resetting specific sensor states, but core workflows are well-supported.
Average 2.7/5 across 50 of 50 tools scored. Lowest: 1.3/5.
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 failing
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.

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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?
The description does not disclose any behavioral traits, side effects, or safety considerations. With no annotations present, the description carries the full burden, but it provides zero information beyond the name.
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?
At two words, the description is too brief to be useful. Conciseness is valued, but not at the cost of omitting essential information. Every sentence should earn its place; here there is no sentence at all.
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 neural simulation and the lack of output schema or annotations, the description is severely incomplete. It fails to inform the agent about return values, behavioral effects, or how this tool fits into the larger system.
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 explain what neuronIds or strength represent. The default value for strength is given but without context (e.g., units, meaning of negative values).
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 "Spike Injection" is a tautology of the tool name, providing no additional clarity. It fails to specify what injecting spikes entails (e.g., into which model or circuit) and does not differentiate from sibling tools like np_send_trigger or np_configure_stim.
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 vs alternatives. There is no mention of prerequisites, typical use cases, or conditions where this tool should be avoided.
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 behavioral traits. It gives no information about side effects, safety, or what happens when commands are invoked (e.g., destruction on stop, permissions needed).
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 three words, but this brevity is at the expense of informativeness. It is not a well-structured description, lacking any detail that would help 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?
Given the tool's simplicity (one parameter with enum) but the lack of output schema and annotations, the description is entirely inadequate. It does not explain the return format for 'status' or contrast with sibling tools like 'get_platform_status'.
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 coverage is 0%, and the description adds zero meaning beyond the enum values. The command parameter's allowed values (start, stop, status) are listed but their semantics (e.g., what 'start' initiates) are not explained.
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 essentially a repetition of the tool name, providing no verb or specific resource. It does not convey what action the tool performs or what it controls, making it a tautology.
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 sibling tools. The description lacks context about scenarios where 'start', 'stop', or 'status' commands would be appropriate, and does not mention any 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?
With no annotations, the description must disclose behavioral traits, but it provides none. There is no indication of whether the tool is read-only, what data it accesses, or 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.
Conciseness1/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but fails to convey any useful information. It is under-specified rather than concise, wasting the opportunity to clarify the tool's function.
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 no parameters and no output schema, the description should at least state its purpose or return type. It is completely inadequate for an agent to use effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the schema coverage is 100% by default. According to guidelines, 0 parameters have a baseline of 4. The description does not need to add parameter info.
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 'Bio-Computing Platforms' is a vague label without a verb or action. It does not specify what the tool does, making it impossible for an agent to understand its purpose.
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 like 'get_system_status' or 'get_metrics'. The description offers no context for decision-making.
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 does not disclose any behavioral traits such as destructiveness, read-only nature, required permissions, or side effects. The description 'Full State Snapshot' gives no behavioral insight.
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 ('Full State Snapshot'), which might be seen as concise, but it is under-specified and fails to provide necessary information. Every word should earn its place; here they do not provide clarity.
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 lack of annotations, output schema, and the presence of 47 sibling tools, the description is grossly incomplete. It does not explain what the snapshot contains, how it differs from other state tools, or how to interpret the output.
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. The baseline for 0 parameters is 4, as there are no parameters to describe. However, the description adds minimal semantic value beyond the schema, but it does not contradict or mislead.
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 'Full State Snapshot' is a tautology of the tool name 'export_snapshot' and fails to specify what state is being exported or what 'snapshot' entails. It provides no verb or resource differentiation, making it unclear what the tool does.
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 sibling tools like get_snn_state or get_system_status. There is no mention of context, prerequisites, or alternatives.
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 carries the full burden of disclosing behavioral traits. It does not indicate whether this tool is read-only, destructive, requires authentication, or has side effects. The description is entirely 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 extremely concise (one phrase) but at the cost of informativeness. It lacks structure and provides no actionable sentences, making it under-specified rather than efficiently concise.
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 absence of output schema and annotations, the description is severely incomplete. It does not explain the tool's functionality, return values, or how to interpret parameters, leaving a critical gap for effective agent use.
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% because the input object has a description 'Olfactory sensor parameters'. However, the tool description itself does not add any meaning beyond the schema. Baseline 3 is appropriate as the schema covers parameters, but no additional value is provided.
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 'Koniku Kore Olfactory Encoding (Chemoreceptor → Latent)' is cryptic and lacks a clear verb or action. It suggests a transformation from chemoreceptors to latent representation but does not explicitly state what the tool does, making it difficult for an agent to understand its purpose.
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 any of its many siblings (e.g., sensor_audio, sensor_visual). The description offers no context for appropriate usage or alternatives.
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 provided and description does not disclose behavioral traits such as state mutation, side effects, or output semantics. Agent cannot infer whether this tool is read-only or destructive.
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?
Extremely concise (2 words) but under-specified. Every sentence should earn its place; here the single phrase fails to provide essential information about purpose or usage.
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 complexity (nested parameter, no output schema, no annotations), description is woefully incomplete. Agent has no understanding of what the tool returns or how to interpret results.
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% so baseline is 3, but description adds no meaning beyond schema. The relationship between the 'action' parameters (targetNeurons, strengths, duration) and violation-of-expectation detection is unexplained.
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 is a noun phrase 'Violation-of-Expectation Detection' rather than a clear verb+resource. It does not specify what the tool does with the input action, and it does not distinguish from siblings like wm_predict or tcai_curiosity.
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 on when to use this tool versus alternatives such as wm_predict or tcai_curiosity. Agent has no context for appropriate invocation.
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 behavior but only offers an abstract phrase. It does not indicate whether the operation is read-only, modifies state, requires permissions, or has side effects. The term 'attention fusion' hints at a machine learning process, but details are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (three words), which is under-specification rather than conciseness. It does not provide enough information for an agent to understand or use the tool effectively.
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 three parameters, no annotations, and no output schema, the description is grossly incomplete. It fails to explain the fusion process, output format, constraints, or integration with sibling tools like sensor_visual or tcai_*.
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 three boolean parameters (includeVisual, includeAudio, includeOlfactory) are self-explanatory, but the description adds no additional meaning. With 0% schema description coverage, the description should at least confirm that these control which modalities to fuse, which it does not.
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 lacking a verb, making it unclear what action the tool performs. It does not specify whether it combines, transforms, or analyzes sensor data, and fails to distinguish it from siblings like sensor_visual or sensor_audio.
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 sensor_visual, sensor_audio, or tcai_capability_model. The description omits context, prerequisites, or conditions for use.
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?
The description provides no behavioral details such as side effects, state mutations, or required permissions. Without annotations, the description should compensate but it is entirely opaque, leaving the agent unaware of what happens when the tool is invoked.
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 at one sentence, but it sacrifices essential detail. It is under-specified rather than efficiently concise, failing to earn its place by omitting crucial 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?
Despite 100% schema coverage, the description lacks context about output, side effects, or how the training step affects the system. For a tool with a nested object parameter and no output schema, this is completely inadequate.
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% with the 'action' parameter described in the schema. The tool description adds no extra meaning beyond what the schema already provides, 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.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Online World Model Training Step' is essentially a restatement of the tool's name and provides no specific action or resource. It does not distinguish this tool from siblings like wm_encode or wm_plan, leaving the agent unclear on what the tool actually accomplishes.
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. There are no conditions, prerequisites, or exclusions mentioned, forcing the agent to guess based on the name alone.
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 only states 'Visual Encoding' but does not reveal whether the tool reads sensor data, generates simulated data, mutates state, or requires permissions. The input parameter 'simulate' hints at simulation, but no side effects or constraints are documented.
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 concise (one sentence) but at the expense of clarity and completeness. It is not structured to front-load key information; instead, it reads as a cryptic label. Over-conciseness here is detrimental.
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's complexity (nested input object, video frames parameter, no output schema), the description is severely incomplete. It lacks information about return values, prerequisites, capabilities, and constraints. The description adds negligible value beyond the tool 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?
Schema description coverage is 100% with each parameter having a description in the schema. The tool description adds context about V-JEPA 2 and image/video processing but does not provide significant additional meaning beyond the schema. Baseline 3 is appropriate as the description complements but does not substantially enhance 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 'V-JEPA 2 Visual Encoding (Image/Video)' vaguely indicates the tool performs visual encoding for images or videos. However, it lacks a specific verb (e.g., 'encode', 'process', 'generate') and does not clearly differentiate from sibling tools like np_camera_capture or sensor_process. The purpose is somewhat clear but not sufficiently specific.
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. There is no mention of prerequisites, typical use cases, or when not to use it. Given the presence of many sensor tools, this omission is significant.
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 behavior but offers minimal information. 'Real-time Metrics' does not clarify if it is a read-only operation, whether it streams data, or if it has side effects. The agent has no insight into its 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?
The description is extremely short (two words), but it is under-specified rather than concise. It fails to provide necessary details, and the brevity comes at the cost of clarity and completeness.
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 output schema, no annotations, and no sibling differentiation, the description is woefully incomplete. It does not explain what metrics are returned, how to interpret them, or how it compares to similar tools, leaving the agent with insufficient 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?
There are no parameters, so the input schema provides full coverage. The description adds no parameter information, but with 0 parameters, the baseline score of 4 is appropriate as parameter semantics are not applicable.
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 'Real-time Metrics' is vague and does not specify what the tool does (e.g., retrieve, stream, or display metrics). It fails to distinguish itself from sibling tools like get_platform_status or get_acm_score, leaving the agent without a clear purpose.
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 lacks context about typical use cases or constraints, making it difficult for an agent to decide when to invoke it.
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 does not disclose behavioral traits such as whether the capture is destructive, read-only, or requires a specific platform state. This is a significant gap.
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 very short but lacks essential detail, making it under-specified rather than appropriately concise.
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 lack of output schema, no annotations, and minimal description, the tool definition is incomplete for accurate invocation by 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 schema already covers the single parameter 'mea' with a description and range. The tool description adds no additional semantic value beyond the schema, which has 100% coverage.
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 it is a 'MEA camera capture' and mentions 'descriptor + viability', which gives some indication of output but does not clearly explain what a 'last capture' means or how it differs from other tools.
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 guidelines provided. The description does not indicate when to use this tool versus alternatives, nor any prerequisites or context for invocation.
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 fails to disclose any behavioral traits such as read-only vs mutating, side effects, or performance implications. The abstract list of concepts gives no insight into 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise (one short phrase), but it lacks a verb or action word that would front-load the purpose. Brevity at the expense of clarity prevents a higher score.
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 tool with no parameters and no output schema, the description should clarify what the tool returns or accomplishes. It merely lists abstract concepts without explaining how the agent should interact with this state.
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?
There are no parameters, so the schema coverage is effectively 100%. The description does not need to add parameter semantics, but it also does not compensate by explaining the output or behavior, which would be expected given the empty schema.
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 lists components of the self-model (interoception, epistemic model, etc.) but lacks a verb to indicate what action the tool performs. It reads as a label rather than a functional description, making it unclear whether this tool retrieves, updates, or processes the self-representation state.
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 compared to siblings like tcai_metaconsciousness or tcai_workspace_state. It does not specify prerequisites or context, leaving the agent to guess.
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 provided, so description must carry full burden. It fails to disclose side effects, state modifications, or required permissions. 'Planning' is ambiguous and doesn't clarify behavioral impact.
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?
Extremely short, but under-specified. Lacks standard structure like action-outcome or usage hint. Conciseness is not beneficial if it sacrifices 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 nested input schema and no output schema, description is insufficiently complete. It doesn't explain planning output, success criteria, or relationship to other wm_* 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%, so baseline is 3. Description adds no extra meaning beyond schema definitions. Parameter descriptions in schema are adequate but not enhanced.
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 'CEM Planning for Optimal Spike Injection' uses vague terms. 'CEM' is undefined, and it doesn't specify what planning entails or how it differs from sibling tools like wm_predict or wm_encode.
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. Lacks context about use cases, 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?
No behavioral traits are disclosed. With no annotations, the description should mention side effects, prerequisites, or state changes, but it only states the action without any 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 concise at 6 words, but it sacrifices completeness. It is front-loaded but fails to convey essential information, making it under-specified.
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 lack of output schema and annotations, and the presence of many sibling tools, the description is severely incomplete. It does not explain return values, prerequisites, or how the prediction relates to other operations.
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 100% of parameters with descriptions, so the baseline is 3. The top-level description adds no extra meaning beyond what the schema already provides.
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 'Predict Next SNN State in Latent Space' clearly indicates the tool predicts the next state of an SNN in latent space, distinguishing it from siblings like wm_encode or wm_plan. However, it is very brief and lacks elaboration on the nature of the prediction.
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 guidelines are provided. The description does not mention when to use this tool versus alternatives like wm_plan or how it fits into a workflow.
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, and the description does not disclose behavioral traits such as read-only behavior, side effects, or authorization needs. The term 'Status' suggests no mutations but 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.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (3 words) but sacrifices necessary information for brevity. It does not earn its place because it lacks clarity and detail.
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 large number of sibling status/metrics tools, the description is critically incomplete. It does not specify what type of world model status or metrics are returned, making it difficult for an AI agent to select correctly.
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?
No parameters exist, so the description could add meaning by explaining what 'World Model Status & Metrics' entails, but it fails to do so. The schema is empty, and the description provides no additional context beyond the name.
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?
Description states 'World Model Status & Metrics' but lacks specificity to distinguish from many sibling tools like get_metrics, get_platform_status, or get_snn_state. The verb 'Status' is implied rather than explicit.
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 like get_metrics or get_system_status. The description does not specify context 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 exist, so description must disclose behavior. It only states an encoding pipeline, omitting critical traits such as whether audio is captured from mic or simulated, side effects, or output format. Behaviors like latency or permissions are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely terse (one sentence). While concise, it lacks necessary details, making it less valuable. Every sentence should earn its place, but this single sentence does not provide enough information to guide usage.
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 a nested input with six parameters, the description is far from complete. It does not explain what the tool returns, processing details, or how to use the parameters effectively. The agent would be left with many unknowns.
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?
Schema description coverage is 100%, but only two parameters (simulate, frequency) have individual descriptions. The tool description adds no extra meaning; 'Waveform → Mel → Latent' does not explain sampleRate or durationMs. With gaps in schema descriptions, the description should compensate but fails.
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?
Description mentions 'Audio Encoding' and the processing pipeline, indicating it encodes audio. However, it is vague on what exactly the tool does (capture? process? return encoded data?), and does not clearly distinguish it from sibling sensor tools like sensor_visual.
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 guidelines provided. Description does not say when to use this tool versus alternatives like sensor_visual or sensor_olfactory, nor does it mention prerequisites or contexts for simulated vs real audio.
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 exist, and the description does not disclose behavioral traits such as side effects, permissions needed, or whether the modification is reversible. The agent cannot assess safety or impact.
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 (3 words), which could be concise, but it omits critical information. It is under-specified rather than efficiently informative.
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 lack of annotations, output schema, and low parameter documentation, the description is incomplete. It does not provide enough context for an 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.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds no meaning to the parameters 'path' and 'value'. It fails to explain what a path represents or what values are acceptable, leaving the agent to guess.
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 'Modify State Parameter' clarifies the action (modify) and target (state parameter) slightly beyond the name, but remains vague. It does not specify what kind of state or parameter, nor does it distinguish from sibling tools like set_parameter vs tcai parameters.
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 or when to avoid it. There is no mention of prerequisites, alternatives, or context for invocation.
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 provided, and the description does not disclose any behavioral traits such as side effects, authorization needs, or rate limits. The tool's impact 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 only five words, which is excessively terse. It sacrifices completeness for brevity, failing to provide meaningful information beyond the tool name.
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 zero parameters and no output schema, the description should clarify the tool's return value and usage scenarios. It does not, leaving users uncertain about what the tool provides.
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 schema coverage is 100%. The description does not add parameter information, but with no parameters, the baseline score of 4 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 'IRB Neural Welfare Check' suggests an ethics/vetting function for neural systems, but is vague. It lacks a specific verb or explanation of what action is performed, making it hard to distinguish from related tools.
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. The description does not mention 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states 'Multimodal Sensor Pipeline Status' without disclosing read-only behavior, side effects, or what the output contains. The agent cannot infer safety or constraints.
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 only three words, but it lacks critical details. While front-loaded, it is too brief to be effective; the tool's complexity and sibling context demand more explanation to 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?
Given no output schema and many sibling tools (e.g., sensor_audio, sensor_visual, get_system_status), the description fails to explain what the tool returns or how it differs. A status tool should describe its output format or scope, which is absent.
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 no parameters, and schema description coverage is 100% (trivially). The description does not add parameter information, but since there are zero parameters, the baseline is 4. The tool's purpose implies no user input is needed, which is consistent.
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, lacking a verb or action. It vaguely indicates the tool provides status but does not clearly state what action the tool performs (e.g., 'get status'). Among sibling sensor tools, it does not distinguish its specific purpose beyond the generic '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 like sensor_audio, sensor_visual, or get_system_status. There is no mention of prerequisites or context for invocation, leaving the agent without decision 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 carries the full burden of disclosing behavior. It merely states 'Advance' without explaining side effects, state changes, blocking behavior, or whether the simulation runs asynchronously. This is insufficient for an AI agent to anticipate consequences.
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 (4 words), but it sacrifices necessary detail. While there is no wasted text, the brevity undermines utility. An ideal description would include at least a brief note about the parameter.
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 simplicity (single parameter, no output schema, no annotations), the description should still clarify what 'advance' entails, e.g., 'Run the simulation forward by a specified number of timesteps.' The current text is too vague to determine if this is the intended meaning.
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 description coverage is 0%, yet the description does not mention the 'steps' parameter or explain its role. The agent must infer from the name and schema alone, which is insufficient for correct invocation.
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 'Advance SNN Simulation' clearly states the action (advance) and the resource (SNN Simulation), indicating the tool progresses the simulation. However, it does not differentiate from siblings like snn_reset or simulation_control, which also deal with simulation state.
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 snn_reset or simulation_control. There is no context on prerequisites, typical scenarios, or exclusions.
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 alone must disclose behavioral traits. It mentions inertia but does not explain its effect, nor does it indicate whether the tool is read-only or modifies state, or any prerequisites.
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 one sentence and concise, but it sacrifices essential details. It is not front-loaded with key 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 output schema, no annotations, and four unexplained parameters, the description is severely incomplete. It does not cover return values, behavior, or usage context.
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%, yet the description fails to explain what each parameter represents or how they relate to PAD dimensions. The term 'raw signals' provides only vague mapping.
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 states the tool appraises raw signals into PAD emotional space (Mehrabian) with inertia, clearly indicating the specific verb and resource. It distinguishes from sibling tools by focusing on emotional appraisal, but does not explicitly differentiate.
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 lacks any contextual cues for appropriate use or exclusion criteria.
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 exist, and the description does not disclose behavioral traits such as read-only nature, side effects, or permissions. The name implies a read operation, but it is not 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 extremely short (one phrase), which is concise but not well-structured as a sentence. It lacks clarity and organization.
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, the description should explain the return value. 'Consciousness Assessment (Proxy)' does not clarify what the tool returns (e.g., a numeric score, status, etc.), making it 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?
There are no parameters, so schema coverage is 100%. The description adds no semantic value beyond the name, but the baseline is 3 since no param info is needed.
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 'Consciousness Assessment (Proxy)' suggests a purpose related to assessing consciousness, but it is vague and does not clearly specify what the tool does (e.g., 'Retrieve the consciousness assessment proxy score'). It is better than a tautology but lacks specificity.
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 guidelines are provided on when to use this tool versus siblings. Siblings include many tcai_* consciousness-related tools, but no distinctions are made.
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 provided, so description must fully disclose behavior. It lacks details on side effects, state changes, authorization, or whether operations are read/write/destructive.
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?
Very concise single sentence, front-loaded with core action. However, it sacrifices specificity for brevity.
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 and no description of return values or effects. In context of many sibling tools, the description is insufficient for a complete understanding.
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?
Input schema has 75% coverage, leaving window_ms undescribed. Tool description adds no parameter-level meaning beyond what schema already provides.
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?
Description gives a high-level purpose (closed loop, read organoid, couple to ASTRA/ROS/ethics, optionally drive SNN) but is vague and doesn't clearly distinguish from sibling tools like inject_spikes 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 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. Does not mention prerequisites, constraints, or exclusions.
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 provided, so description must disclose behavioral traits. It explains state value meanings but does not state whether the tool mutates state or is read-only. Side effects are unclear.
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?
Only two sentences with no unnecessary words. However, the core action (get/update state) is not front-loaded.
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 description should hint at return values or effects. It explains state dynamics but omits what the tool actually returns or changes, making it 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 coverage is 100% and schema description for 'rpe' already covers range and purpose. Description adds minimal value by contextualizing injection as part of meta-learning state, but 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 mentions 'meta-learning state' and interprets velocity and noveltySpike, but does not clearly state a verb+resource (e.g., 'get meta-learning state' or 'update meta-learning state'). It is somewhat vague and does not differentiate from sibling tools.
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 guidance on when to use this tool vs alternatives. The phrase 'Optionally inject an RPE sample' hints at usage but does not provide clear context or exclusions.
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 provided, so description must carry full behavioral disclosure. It only implies a read operation ('status') but lacks details on safety, side effects, or what the status entails (e.g., format, scope).
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?
Extremely short and front-loaded, but unduly sparse. A single phrase sacrifices substance for brevity, lacking necessary detail for a useful tool 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 zero parameters and no output schema, the description is incomplete given the complex sibling set. It fails to explain what the status includes, how it differs from similar tools, or how to interpret results.
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 zero parameters, the schema fully covers parameter needs. The description adds no param info, but the baseline for 0 params is 4; no credit lost or gained.
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 'ASTRA System Status' indicates the tool retrieves system status but is vague and does not differentiate from sibling tools like get_platform_status or get_metrics. It adds minimal specificity 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 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 (e.g., get_platform_status, get_metrics), no context about prerequisites or expected outcomes, leaving the agent without decision 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 provided, the description must fully disclose behavioral traits. 'Reset SNN Engine' is too vague; it doesn't mention side effects (e.g., data loss), authorization needs, or impact on other components. The agent cannot assess safety or reversibility.
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 sentence, which is concise, but it could convey more meaning without extra length. It is not front-loaded with the most critical information, though brevity is a positive.
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 potential impact (resetting an engine), the description is incomplete. It does not explain what happens after reset (e.g., state restoration, logging), nor does it provide output schema context. More detail is needed for an agent to use it 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?
No parameters exist, so the description does not need to explain them. Schema coverage is trivially 100%. Baseline for zero-parameter tools is 4; no additional information is required.
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 'Reset SNN Engine' clearly identifies the action (reset) and resource (SNN Engine), but lacks specificity on what 'reset' entails—e.g., state reinitialization, parameter clearing, or full system restart. Among siblings like tcai_reset, no differentiation is provided.
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 or why to use this tool versus alternatives. The description does not state prerequisites, exclusions, or typical scenarios, leaving the agent to guess appropriate invocation 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 carries the full burden of disclosing behavioral traits. It only states 'SNN Engine State,' which implies a read operation but does not explicitly confirm read-only behavior, required permissions, or potential side effects. The lack of detail makes it insufficient for an agent to understand implications.
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 at three words, which is efficient for a simple tool. However, it is so minimal that it adds little value beyond the tool name. Structure is adequate but could benefit from additional context to 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?
Given the tool's role as a state retrieval mechanism and the absence of an output schema, the description fails to specify what data the state contains or how it differs from sibling tools like get_platform_status. The context is incomplete for an agent to reliably distinguish usage.
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 description need not add meaning to parameters. Per calibration, a baseline of 4 applies when no parameters exist. The description does not contradict this baseline and is adequate for the parameterless context.
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 'SNN Engine State' clearly indicates the tool retrieves the state of the SNN engine, establishing a specific verb-resource pair. However, it does not differentiate from sibling tools like get_platform_status or get_system_status, which also retrieve states. The description is minimal and borders on tautology.
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 get_metrics or snn_reset. There is no mention of context, prerequisites, or exclusions, leaving the agent without clear usage boundaries.
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 exist, and the description only states the tool returns status. It fails to disclose behavioral traits such as whether it is read-only, permission requirements, or response characteristics, placing full disclosure burden on the description.
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, consisting of a single phrase. While there is no wasted text, it could be slightly more descriptive without harming conciseness. The structure is front-loaded but overly minimal.
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 presence of many sibling tools and no output schema or annotations, the description is incomplete. It does not specify what 'status' includes, how the output is structured, or any limitations. For a simple zero-param tool, the description is minimally adequate but lacks needed context.
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 schema coverage is 100% trivially. With no parameters, the description cannot add parameter-level meaning, but the lack of parameters makes this dimension less critical; thus baseline score of 4 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 'NeuroPlatform v2 — Platform & Controller Status' indicates the tool retrieves status information, but lacks a specific verb (e.g., 'get') and does not distinguish it from sibling tools like 'get_platform_status', which likely serves a similar purpose.
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. Given many sibling tools, the absence of usage context reduces the agent's ability to select the correct tool.
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 carries full responsibility for behavioral disclosure. It only states basic functionality (spikes per electrode over a window) but omits that the tool reports the top-k most active electrodes (as indicated by the top_k parameter) and any side effects or safety considerations. Score 2 reflects 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, making it concise. However, the use of underscores and colons makes it slightly less readable. Score 4 for being concise but not perfectly 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?
There is no output schema, so the description should explain what the tool returns. It does not mention return values, the top-k filtering, or the structure of results. For a simple tool with two parameters, this is a significant gap. Score 2 indicates incompleteness.
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 100% description coverage, so both parameters are already documented. The description adds minimal new meaning beyond referencing the window parameter. 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 states that the tool counts spikes per electrode over an N-ms window, which is clear and specific. However, it does not differentiate from similar siblings like np_query_spike_count, and the mention of 'Closed-loop' may be confusing. A score of 4 reflects good purpose clarity with minor room for improvement.
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. Sibling tools like np_query_spike_count exist but no distinction is made. A score of 2 indicates minimal usage guidance.
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 provided. Description only mentions querying spike timings, with no disclosure of behavioral traits like read-only nature, performance, or 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is very short (one sentence) but misses important details. It is concise but at the expense of completeness.
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 3 parameters, no output schema, and many siblings, the description is insufficient. It does not explain return format, relationship to other spike tools, or usage context.
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?
Only window_sec has a schema description; fsname and limit lack descriptions. The tool description does not add any explanation for these parameters to compensate for the 33% 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?
Description clearly states it queries individual spike timings over a time window, using specific verb and resource. It distinguishes from sibling tools like np_query_spike_count (counts) and np_query_triggers (triggers).
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 vs alternatives. Does not explain that it returns individual events vs aggregated counts or triggers.
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 bears full responsibility for behavioral disclosure. It states the tool is a query (implying read-only) but does not describe any behavioral traits such as cost, rate limits, or side effects. Minimal transparency beyond the basic purpose.
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 that is front-loaded and concise with no waste. It could be slightly more informative without losing conciseness, but it achieves the minimum efficiently.
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 has no output schema, so the description should explain what is returned. It does not mention the return format or any side effects. With only two parameters and a simple query, the description lacks completeness about outputs and behavioral notes.
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 both parameters with descriptions. The tool description adds no additional meaning beyond referencing 'over a window', which is already implied by window_sec. Baseline 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 queries triggers sent to the organoid over a window, using a specific verb and resource. However, it does not explicitly distinguish it from related sibling tools like np_query_spike_events or np_send_trigger, lacking differentiation.
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 when-not conditions or sibling tool comparisons are mentioned, leaving the agent without context for selection.
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 only states a high-level purpose without mentioning side effects, permissions, rate limits, or what happens during processing (e.g., reading vs simulating). Minimal 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 extremely concise, a single sentence with no filler. However, it may be too brief to provide sufficient context; a bit more detail could be added without losing conciseness.
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 a multimodal pipeline with three optional parameters and no output schema, the description is incomplete. It does not explain the output format, defaults, or how to use parameters effectively. Much more context is needed.
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 fully cover the three parameters (visualSource, audioFrequency, compounds) with clear explanations. The description adds the context that all modalities are fused, but does not elaborate on how parameters affect the output. Given 100% schema coverage, baseline 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 'Full Multimodal Pipeline (All Modalities → Fused z)' clearly indicates the tool processes all sensor modalities and fuses them, distinguishing it from individual sensor tools like sensor_audio, sensor_visual, and sensor_olfactory. However, it lacks a specific verb and elaboration on what 'fused z' means.
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 guidelines are provided about when to use this tool versus alternatives like sensor_fuse or individual sensor tools. The description offers no context for appropriate usage or exclusions.
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 should fully disclose behavioral traits. It hints at internal processing ('attention-gated, salience-indexed') but fails to state whether the operation is idempotent, persistent, or destructive. Side effects and response behavior are omitted.
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 of 10 words, achieving high efficiency. However, it is too brief for the complexity of the tool, sacrificing informative content for brevity.
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 6 parameters, no output schema, and no annotations, the description is insufficiently complete. It does not explain return values, error conditions, or the effect of the 'store' operation beyond the basic purpose.
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% (5 of 6 parameters described). The description adds no additional meaning to the parameters beyond what the schema provides. It does not explain how concepts like 'attention' or 'salience' relate to the parameters, nor does it compensate for the undocumented 'attentionLevel' parameter.
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 'Store an experience in emotional memory' clearly identifies the action (store) and the resource (emotional memory). The additional terms 'attention-gated, salience-indexed' add specificity. It distinguishes from sibling tools like tcai_memory_retrieve, though not explicitly.
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_emotion_appraise or tcai_capability_model. There is no mention of prerequisites, constraints, or when not to use it.
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 carries full burden. It does not disclose whether the tool modifies state, requires permissions, or is read-only. Behavioral traits are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise but under-specified. It lacks front-loaded key details like output or side effects, making it borderline too terse.
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 SNN encoding and no output schema, the description is incomplete. It does not explain what 'latent space' refers to, what the tool returns, or how it fits into the broader SNN workflow.
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 schema coverage is 100%. The description does not need to add parameter meaning, and it is sufficient to indicate no arguments are required.
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 action (Encode) and the resource (SNN State to Latent Space). It is specific, but does not differentiate from sibling tools like wm_predict or wm_plan, which could also involve transformations.
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. Sibling tools include many wm_* operations (e.g., wm_predict, wm_plan), and the description provides no context for selection.
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 fully disclose behavior. It mentions 'execute uploaded StimParams' but does not specify whether the tool is read-only or destructive, what happens on repeats, potential side effects (e.g., affecting ongoing experiments), or error states. The agent is left guessing about operational impact.
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?
A single, well-constructed sentence that front-loads the key purpose and mechanism. However, it could benefit from additional structure (e.g., a second sentence for usage notes) without being verbose. Every phrase 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?
For a tool with no annotations or output schema, the description is minimal. It omits prerequisites (e.g., how to upload StimParams), expected return values (e.g., success confirmation), and error conditions. Given the complexity of the domain (neurostimulation), the agent needs more detail 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 schema covers 100% of parameters, but the description adds value by explaining that triggers 'execute uploaded StimParams' and that the array is 16-bit. This gives meaning beyond the schema's raw integer range, clarifying the domain-specific purpose of 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 clearly states the tool fires triggers to execute uploaded StimParams via a 16-bit trigger array. It uses specific verbs ('fire') and resources ('trigger(s)'), and the context of being part of NeuroPlatform v2 helps distinguish from general trigger tools, though sibling differentiation is not explicit.
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 like np_query_triggers or np_configure_stim. There is no mention of prerequisites (e.g., StimParams must be uploaded) or scenarios where this tool is appropriate. The description lacks when-not or alternative references.
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 bears full responsibility for behavioral disclosure. It mentions the retrieval algorithm but omits important details such as whether the tool is read-only, modifies memory state, requires prior embeddings, or what happens with insufficient matches. The term 'PAD' is domain-specific and not explained.
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, concise and to the point. It avoids unnecessary words. However, it could be more structured by separating the method from the core action, but it's still efficient for a retrieval 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?
Given the tool has optional parameters, no output schema, and a potentially complex retrieval mechanism (blended similarity, PAD), the description is too brief. It does not explain the return format, range of results, or how parameters interact. An agent might need more context to use it correctly without additional troubleshooting.
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 high (80%: 4 out of 5 parameters have descriptions). The description does not add any parameter-level detail beyond the schema. For topK, the schema provides min/max constraints but no semantic description; the description misses an opportunity to explain its role. Baseline score of 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 purpose: 'Retrieve memories' with specific retrieval method ('blended cosine similarity, PAD congruence and salience'). This distinctly differentiates it from sibling tools like tcai_memory_store or wm_encode, providing a precise verb+resource+scope.
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 (e.g., tcai_memory_store for storing, wm_predict for prediction) or when not to use it. The description lacks any context about prerequisites or typical usage 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 has full burden to disclose behavior. It implies a read operation but does not explicitly state it's non-destructive, lacks side effects, or require any permissions. The brief statement omits critical behavioral traits like idempotency or rate limits.
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 concise at one sentence, listing key state components. However, it is somewhat dense and could benefit from clearer structuring (e.g., 'Returns: ...') to improve scannability. Still, it earns its place with no wasted words.
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 simple state retrieval tool with no output schema, the description provides some context (the metrics returned) but is incomplete. It does not specify the return format, data types, or whether it returns a single value or an object, leaving ambiguity for the 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, and schema coverage is 100%. Per guidelines, no parameters gives a baseline of 4. The description does not add parameter details because none exist, so the score remains 4.
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 'Global Neuronal Workspace state' and lists specific metrics (ignition, focus, qualia, sync R, unity metrics, access history), indicating it retrieves state information. However, the jargon-heavy terms like 'qualia' and 'sync R' may reduce clarity for an AI agent, making it slightly less than a 5.
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 its siblings (e.g., get_snn_state, get_metrics). There's no mention of context, prerequisites, or alternatives, leaving the agent to guess the appropriate scenario.
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 must fully disclose behavioral traits. It mentions defining, validating, and uploading, but lacks details on side effects (e.g., overwriting existing configs), error handling, or platform state requirements.
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, clear sentence with no unnecessary words. It efficiently conveys the core purpose and includes version info.
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 complexity (11 parameters, no output schema), the description is minimal. It does not cover return values, workflow context, or how this tool integrates with other NeuroPlatform tools. Lacks sufficient detail for effective agent use.
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 high (82%), so baseline is 3. The tool description does not add extra meaning beyond summarizing the purpose; it fails to explain parameter interactions or provide usage examples.
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 defines, validates, and uploads a StimParam for charge-balanced biphasic stimulation, with specific verbs and resource. It distinguishes from sibling tools (none directly similar).
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 vs alternatives, no prerequisites, exclusions, or context provided. The description only states what the tool does, not when it should be used.
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 provided, so description carries full burden. It states it returns a rate but does not disclose read-only nature, side effects, or behavior for empty results. Minimal behavioral insight beyond basic functionality.
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?
Single sentence efficiently conveys purpose, resource, and output type. No redundant words; includes system context (NeuroPlatform v2 DB).
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?
Query tool with 3 parameters and no output schema needs more context. Description does not explain return format, effect of nonzero_only, or simulation clock relationship. Incomplete for agent to fully understand usage.
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 covers 67% of parameters with descriptions. Description adds context that the tool returns spikes per minute over a time window but does not elaborate on parameter implications or add significant new meaning beyond schema.
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?
Description clearly states it queries spikes per minute per electrode over a time window, distinguishing it from sibling tools like np_count_spikes and np_query_spike_events. The verb 'query' is implied and resource 'spike count' is specific.
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 vs alternatives like np_count_spikes or np_query_spike_events. No mention of context 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 provided. The description does not disclose any behavioral traits (e.g., whether it is read-only, expensive, or state-dependent). It only states it produces a report, which implies a retrieval operation but lacks explicit safety or performance characteristics.
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 sentence that front-loads the tool's purpose and key metrics. No wasted words; every element adds meaning.
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?
Given no output schema and many sibling tools, the description is minimal but lists the metrics included. It lacks details on what each metric represents or how the report can be used, so completeness is adequate but not thorough.
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, so the schema fully covers parameter definitions. The description does not need to add parameter semantics, earning a baseline score of 4.
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 produces a 'Consciousness proxy report' listing specific metrics (GNW metrics, Effective Information, Φ̃-RIIU, composite score), distinguishing it from sibling tools like get_metrics or other tcai_* tools.
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 (e.g., get_metrics or tcai_* tools). The agent receives no contextual cues for selection.
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 discloses that the tool returns stage, stability, and meta-representation scores, and mentions second-order self-monitoring over time. However, it does not explicitly state that the tool is read-only, non-destructive, or what side effects might occur. The lack of side-effect disclosure limits 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 concise, consisting of a single sentence that front-loads key information. However, it uses domain-specific jargon that might reduce clarity for some agents, slightly detracting from conciseness.
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?
Given the tool's complexity as a longitudinal tracker with multiple outputs, the description provides only a high-level enumeration of outputs (stage, stability, meta-representation score). Without an output schema, it lacks details on data types, ranges, or format, which are necessary for correct invocation and interpretation.
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 no parameters, and the schema coverage is 100% (trivially). The description does not need to explain parameters. The baseline score of 4 is appropriate as there is no parameter information to provide.
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 states that the tool tracks longitudinal development, outputting coarse stage, stability, and meta-representation score. It distinguishes from sibling tools like tcai_metaconsciousness by focusing on developmental tracking over time. However, the jargon (e.g., 'composite-proxy level') may obscure clarity for non-experts.
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 indicate when to use this tool versus alternatives, nor does it mention prerequisites or contextual cues. The agent must infer usage solely from the tool 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The term 'snapshot' implies read-only behavior, but the description does not explicitly state that it is non-destructive or lacks side effects. Without annotations, this is a mild 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?
Two sentences efficiently convey the tool's purpose. The first sentence lists key metrics, the second adds context. Could be slightly tighter but overall 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 description lists the metrics covered but does not specify the output format (e.g., whether they are returned as separate fields). Lacking output schema, more detail on the response structure would improve completeness.
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?
No parameters exist, so parameter semantics are not applicable. The high schema coverage (100%) means the description does not need to add parameter 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 it is a 'snapshot' of second-order loop metrics, listing components like meta-learning velocity and RND curiosity. It conveys a distinct purpose as a composite metric, but does not explicitly differentiate from sibling tools that return individual 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 on when to use this tool versus alternatives like tcai_curiosity or tcai_meta_learning. The description lacks context for tool selection.
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 burden. It mentions it's a proxy, not a measurement, but does not disclose whether it is read-only or has side effects. No information about permissions or data persistence.
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 conveying purpose, components, and caveat. No extraneous content, front-loaded with key information.
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?
Lacks details on return format (e.g., score range, type) and usage context. Since there is no output schema, the description should clarify the output, but it does not.
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?
Input schema has zero parameters, and schema coverage is 100%. Per guidelines, baseline is 4; description adds no parameter info because none exist.
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?
Description clearly states it provides a weighted score over four specific components (confidence calibration, learning awareness, self-continuity, error monitoring) and identifies itself as a proxy. This differentiates it from other tcai_* tools.
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 like tcai_self_model or tcai_second_order. The description does not specify prerequisites or 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?
No annotations are provided, so the description carries the full burden. It states the tool queries or lists, but does not disclose behavioral traits such as idempotency, side effects, or any state requirements. For a read-like operation, minimal disclosure is insufficient.
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, front-loaded with the tool's identity and followed by its two use cases. Every word serves a purpose; 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's simplicity (1 optional parameter, no output schema), the description adequately covers its dual mode. However, it lacks details on return format or possible errors, leaving minor gaps. The high schema coverage offsets the need for extensive description.
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 coverage is 100% for the single parameter 'action', which is described as 'Action label to query expected valence for'. The description adds value by implying that omitting the action parameter results in listing the capability table, a semantic nuance not captured in the schema.
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 purpose: 'Agency capability model... Query expected outcome of an action, or list the learned capability table.' It uses specific verbs ('query', 'list') and identifies the resource (expected-valence map), distinguishing it from sibling tools focused on sensors, simulation, or other tcai functions.
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 for obtaining expected valence for an action or listing the table, but it does not provide explicit guidance on when to use this tool versus alternatives like get_acm_score or other query tools. No when-not-to-use or contextual prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description explains the internal computation (prediction error, random target, online predictor) and defaults to GNW broadcast. However, it does not specify the output format or side effects, leaving a minor 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 two sentences, front-loading the core function and defaults. Every phrase is informative with no 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?
While the description explains the mechanism and defaults, it does not explicitly state the return value (e.g., curiosity score) or provide usage context among siblings. Given no output schema, this is a notable gap.
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 parameter 'embedding' is fully described in the schema. The description adds value by explaining its default (current GNW broadcast) and role in curiosity computation, enhancing understanding.
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 computes curiosity as prediction error between a frozen random target and an online predictor, referencing RNDCuriosity port and epistemic value. It distinguishes itself from sibling tools by specifying the unique mechanism.
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 tcai_active_inference or tcai_metaconsciousness. The description lacks context for tool selection.
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 provided, the description carries the full burden for behavioral disclosure. It specifies the tool returns telemetry data and lists the quantities. However, it does not explicitly state that the tool is read-only, non-destructive, or disclose any side effects, rate limits, or prerequisites. The technical nature implies a query operation, but the absence of explicit transparency statements is a 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 sentence, which is concise. It front-loads the key outputs and includes citations for authority. However, the heavy jargon and references may reduce readability for an AI agent. Every clause earns its place, but the density could be slightly detrimental to quick parsing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has no parameters and no output schema, the description must fully explain what it returns. It does so comprehensively by listing all the core telemetry quantities: F, G(π) decomposed, task quality, model entropy, and Dirichlet-learned action. This is complete for the tool's purpose, and no further context is needed.
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 with 100% coverage. The description adds meaning by detailing what the tool returns, which is helpful for interpreting the output. Since there are no parameters to document, the baseline score of 4 is appropriate, and the description provides value beyond the schema by explaining the returned quantities.
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 provides 'Active-inference core telemetry' listing specific quantities (F, G(π), task quality, etc.). It distinguishes itself from sibling tools by emphasizing it contains the 'principled quantity the halting criterion thresholds on' rather than heuristic correlates. The verb is implicit but the resource and scope are unambiguous.
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 obtaining the genuine variational free energy and expected free energy used for halting decisions, contrasting with heuristic correlates. However, it does not explicitly state when to use this tool versus alternatives like tcai_metrics or tcai_convergence, nor does it provide explicit when-not-to-use guidance. The usage context is suggested but not fully delineated.
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 carries full burden. It states what is reset but does not disclose whether the action is destructive, reversible, or requires permissions. For a reset tool, more context on side effects would improve transparency.
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 conveys the action and scope with no unnecessary words. Every part earns its place.
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 zero parameters, no output schema, and a straightforward reset action, the description is largely complete. However, it could mention the effect on active processes or user data for full completeness.
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 schema description coverage is 100%, so baseline is 4. The description does not need to add parameter information.
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 verb 'Reset' and the resource 'TCAI consciousness system', and lists specific components (workspace, memory, emotion, metrics). This distinguishes it from sibling tools like 'snn_reset' which resets 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for resetting the TCAI system but provides no explicit guidance on when to use it over alternatives, or any prerequisites. It does not mention when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description provides good behavioral detail: the pipeline stages, early stopping via stopWhenSatisfied, and description of threshold parameters. It does not explicitly state whether the operation modifies internal state, but the pipeline implies changes to memory and self-model.
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, highly efficient. First sentence lists the pipeline steps, second explains the early stopping. No extraneous information. Front-loaded with the core action.
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?
While the pipeline and stopping criteria are explained, the description lacks information about return values, side effects (e.g., memory writes), and prerequisites. Given 15 parameters and no output schema, more guidance on results would improve completeness.
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 coverage is 80%, so baseline is 3. The description adds context by explaining the stopWhenSatisfied mechanism and how threshold parameters (epsFreeEnergy, relFreeEnergy, etc.) relate to convergence. This adds value beyond the parameter descriptions in the schema.
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?
Description clearly states the tool runs ACM cycles and lists the full pipeline (SNN signals through second-order loop). The verb 'Run' with the specific resource 'ACM cycles' differentiates it from sibling tools like tcai_active_inference or tcai_curiosity.
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?
Implied usage: run cycles when you want to simulate the consciousness pipeline. However, no explicit guidance on when to use this tool vs alternatives (e.g., tcai_active_inference, tcai_self_model). No exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 discloses that the tool both reads and writes configuration, and explains the halting logic in detail. However, it does not mention side effects, permissions, or rate limits, which would be beneficial but are not critical for this read/write 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 three sentences with no wasted words. Front-loaded with the main purpose, then immediately clarifies the two modes (inspect vs configure), and finally explains the halting condition. Every sentence earns its place.
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's simplicity (no output schema, all optional params), the description is quite complete. It explains the halting logic and parameters. It could mention return format or persistence of updates, but the current text is sufficient for competent use.
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 baseline is 3. The description provides the overall halting logic but adds no per-parameter meaning beyond what the schema already offers (e.g., thresholds and defaults). Thus, it meets the baseline without extra value.
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 inspects or configures the recursive double-loop halting criterion (v2.7), with specific verbs 'inspect' and 'configure'. It distinguishes from sibling tcai_* tools by focusing uniquely on the convergence halting logic.
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 explains when to use the tool: to check the current state (no arguments) or update thresholds (with arguments). It provides detailed context on the halting condition but does not explicitly state when not to use it or name alternative tools, leaving usage partly implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It discloses that the tool runs warm-up cycles, records ΔF, and sets epsFreeEnergy (a side effect). However, it does not mention if this is destructive or reversible, but the core behavior is well explained.
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 main purpose, and no filler. Every sentence adds value, including the historical context (v2.7 critique).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 3-parameter tool with no output schema, the description covers the process, return values (measured ΔF scale and threshold), and even addresses prior critique. No significant gaps.
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 coverage is 100% with parameter descriptions. The tool description adds context by stating defaults and explaining how parameters interact (cycles as warm-up, factor multiplying median). This adds value beyond schema alone.
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 calibrates the halting threshold by measuring ΔF and setting epsFreeEnergy to factor times median |ΔF|. It distinguishes from siblings by focusing on calibration rather than general active inference or metrics tools.
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 implies usage when the default threshold is uncalibrated (v2.7 critique), but lacks explicit when-to-use or when-not-to-use compared to siblings like tcai_cycle. Still provides clear context.
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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