ASTRA MCP Server

Server Quality Checklist

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Latest release: v2.5.0
Disambiguation4/5
Tools are grouped by domain (tcai, wm, sensor, snn, np) and each description specifies a distinct subsystem or function. Some overlap exists among reporting tools (e.g., tcai_metrics, tcai_second_order, tcai_metaconsciousness), but their detailed descriptions clarify differences. Overall, an agent can usually select the right tool with careful reading.
Naming Consistency2/5
Naming conventions are inconsistent. Some tools use verb-first patterns (get_system_status, set_parameter, export_snapshot), others use domain-prefix + noun (np_status, sensor_status, tcai_self_model), and still others use domain-prefix + verb (wm_encode, tcai_reset, sensor_fuse). While the domain prefixes help organize tools, the lack of a uniform verb_noun pattern makes the set feel chaotic.
Tool Count2/5
With 49 tools, the server feels overloaded. The broad scope (SNN simulation, world model, sensors, consciousness metrics, organoid control) justifies a larger toolkit, but the sheer number likely overwhelms agents and increases selection errors. A more streamlined set or grouped sub-servers would be more appropriate.
Completeness4/5
The tool surface covers the major subsystems comprehensively: sensor encoding and fusion, world model training/prediction/planning, SNN stepping and reset, consciousness cycle and metacognition, memory storage/retrieval, and neuroplatform stimulation/queries. Minor gaps exist (e.g., no explicit raw SNN waveform export, no tool to delete memories), but agents can accomplish core workflows.
Average 2.6/5 across 49 of 49 tools scored. Lowest: 1.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
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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.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. It says nothing about side effects, whether it mutates neuron state, requires an active simulation, or what 'strength' actually does. This is a critical gap for a tool that likely modifies neural state.
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 under-specified at only two words, which is not conciseness but a lack of content. It fails to earn its place and provides no useful information beyond the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with two parameters, no annotations, and no output schema, the description is critically incomplete. It does not explain the operation's effect, prerequisites, or what the agent should expect as a result, making safe invocation impossible.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameter meanings, but it does not. The schema's property names (neuronIds, strength) offer minimal hints, but the description adds zero semantic value, leaving the agent to guess the role of each parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Spike Injection' is a tautology that merely restates the tool name 'inject_spikes' without using an action verb or specifying the target resource. It fails to differentiate from sibling tools like np_send_trigger or np_configure_stim, which likely serve similar functions in the same domain.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not state when to use this tool, what preconditions exist, or how it differs from alternatives. The agent is left without any 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.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It gives no indication of side effects, safety, permissions, or state changes. The tool name implies operational control but does not reveal whether actions are destructive, reversible, or how they affect the system.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is minimal in length, which could be considered concise, but it is severely under-specified. It does not earn its place as it contributes no actionable information. While there is no verbosity, the lack of substance makes it ineffective rather than 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 tool's simple schema but lacking annotations and output schema, the description is wholly inadequate. It does not mention what commands do, what states exist, or what responses look like. The tool appears to be a control interface, yet the description fails to provide even basic operational 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?
The schema has one parameter, 'command', with an enum of start/stop/status, and the description provides zero additional meaning. Despite low schema description coverage (0%), the description fails to compensate by explaining command semantics, expected outcomes, or relationships. The enum is self-explanatory, but the description adds nothing beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Simulation Control' is a direct restatement of the tool name. It fails to specify any verb or resource, and instead merely labels the tool's domain. No distinction is made from sibling tools like wm_status or get_platform_status, leaving the agent to guess what 'control' means.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance whatsoever on when to use this tool versus alternatives. The description omits any contextual cues such as typical scenarios, preconditions, or exclusions. An agent cannot determine whether this is the appropriate tool for starting, stopping, or querying a simulation without additional information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description offers zero behavioral disclosure. It does not mention side effects, data formats, return values, or any operational context, leaving the tool's runtime behavior 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 extremely terse (one phrase), but this is under-specification rather than effective conciseness. It does not use the space to convey meaningful content, and the lack of structure (no sentences, no guidance) makes it essentially a title.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, and a description that only repeats the tool's topic, the tool is drastically underdescribed. It fails to explain what the tool does, what inputs mean (beyond schema), or what results to expect, making it unusable for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, as every parameter (input, videoFrames, width, height, source, channels, simulate) has a description in the schema. The baseline of 3 is appropriate because the description adds no parameter information, but the schema already documents all parameters.
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 'V-JEPA 2 Visual Encoding (Image/Video)' is a noun phrase, not a statement of what the tool does. It lacks an action verb and merely restates a topic related to the tool name 'sensor_visual', providing no clarity on the tool's actual function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. Sibling tools like sensor_audio, sensor_fuse, and wm_encode exist, but the description gives no indication of the use cases, prerequisites, or differentiation needed for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry the full burden of behavioral disclosure. It only states the name, leaving the agent clueless about side effects, mutation, or return 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, but it is under-specified rather than concise. It is a phrase without a verb and conveys no meaningful instructions, so it does not earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This tool has a nested parameter object, no output schema, and no annotations. The description is entirely inadequate for an agent to infer behavior, timing, or expected outcomes.
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 detailed explanations for the nested action object's properties (targetNeurons, strengths, duration). The tool description adds no parameter information, but the baseline 3 is justified because the schema already handles it.
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 is a noun phrase that restates the tool name without a verb or resource. It does not clarify what a 'training step' executes, nor distinguish itself from siblings like wm_encode or wm_predict.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus the many related wm_* or snn_* tools. There is no mention of prerequisites, integration points, or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full responsibility for disclosing side effects, return values, or access requirements. The description provides zero behavioral information beyond the name, which itself is only a hint. It does not even state that it returns status.
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 under-specified, consisting of only three words that form a fragment. While brevity can be a virtue, this is not concise communication; it lacks structure and fails to form a complete thought. It is similar to the 'Process' example.
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 params, no output schema, and no annotations, the description should at least clarify what the status represents, what platform it refers to, and how it relates to sibling status tools. It provides none of that, making it completely inadequate for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema confirms this. Since there is nothing to document about parameters, the description does not need to elaborate. The baseline of 4 for no parameters applies here; the description does not add param semantics but also does not need to.
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 noun phrase, not a verb+resource statement. It does not say what the tool does or how it differs from siblings like get_system_status or wm_status. It is essentially uninformative and borders on a misleading fragment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as get_system_status, get_metrics, or wm_status. No context or exclusions are provided, 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.
Behavior1/5
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 discloses nothing: no side effects, no state changes, no return behavior, no dependencies. For a fusion tool, the agent would need to know whether this performs a read-only computation, modifies internal state, or requires prior sensor setup. Complete lack of behavioral information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (three words), but this is under-specification, not concise clarity. It lacks any sentence structure that conveys action or context, and every word is a technical buzzword rather than useful guidance. A concise description would still explain 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?
For a tool with three parameters, no output schema, no annotations, and a complex domain (cross-modal fusion), the description is wholly insufficient. It does not explain return values, side effects, use cases, or how the inclusion flags affect behavior. The agent cannot correctly select or invoke the tool based on this description.
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 0% and the description does not mention any parameters. Although the parameter names (includeAudio, includeVisual, includeOlfactory) are self-descriptive, the description adds no meaning, leaving the agent to infer that these booleans toggle modality inclusion. The phrase 'Cross-Modal' hints at multiple modalities but does not clarify the flags' roles or defaults.
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, not a clear verb+resource statement. It vaguely implies combining sensor modalities with attention, but does not explicitly say what the tool does or how it differs from siblings like sensor_process or individual sensor tools. This is closer to a tautology of the tool name 'sensor_fuse'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool vs alternatives. It neither states a use case, prerequisites, nor contrasts with sibling tools such as tcai_meta_learning or sensor_visual. The description provides no context for selecting this tool.
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 discloses no behavioral traits—whether this is a read-only analysis, whether it modifies internal state, how it responds, or any side effects. The phrase gives no operational detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than effective conciseness. It provides only a six-word phrase and omits essential information; it does not 'earn its place' as a useful explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with a required nested action object and no output schema or annotations, this description is completely inadequate. It does not explain what the tool does, what inputs mean, what outputs look like, or when to use it, leaving the agent without enough context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully describes the nested 'action' object and its properties (targetNeurons, strengths, duration), so schema coverage is 100%. However, the tool description itself adds no parameter meaning—it does not explain how these parameters relate to surprise detection.
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 'Violation-of-Expectation Detection' is a noun phrase that essentially restates the tool's name (surprise = violation of expectation). It lacks an action verb and does not explain what operation the tool performs, making it hard for an agent to know what invoking it will do.
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 siblings like wm_predict or wm_encode. The description gives no context, prerequisites, or alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing behavioral traits such as side effects, safety, or return behavior. The description offers none of this, revealing nothing about 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 very short, but this is not appropriate conciseness; it is under-specification. 'IRB Neural Welfare Check' is a bare phrase that omits essential information, so it does not earn credit for being well-structured or front-loaded with useful content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no parameters and no output schema, the tool's description should still explain its purpose and expected results. This description is completely inadequate, giving no sense of what the tool does, when to use it, or what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is trivially complete (100% coverage). Per the rubric, 0 parameters earns a baseline of 4. The description does not need to add parameter meaning since there are none.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'IRB Neural Welfare Check' is essentially a restatement of the tool name 'check_ethics' with added jargon. It does not specify an action verb or clearly state what the tool does, making it nearly tautological and unhelpful for distinguishing this tool from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool or how it differs from the many sibling tools. The description provides no context for selecting this tool over alternatives, leaving the agent without any usage criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Real-time' hints at temporal behavior but reveals nothing about side effects, data scoping, rate limits, authentication, or return format. This is completely insufficient for a tool with no other metadata.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The two-word description is extremely short, but this is under-specification rather than effective conciseness. It omits all substantive information and does not earn its place as a useful description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description is the only source of context. It does not explain what 'metrics' means, what the return structure looks like, or what 'real-time' implies operationally. The tool is effectively opaque to an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, making the schema coverage trivially 100%. Per the rubric, 0 params yields a baseline of 4, and the description does not need to add parameter semantics because there are none.
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' essentially restates the tool name 'get_metrics' without a verb or specific resource. It vaguely suggests the tool returns metrics in real time but provides no detail on what metrics or how this differs from sibling tools like tcai_metrics or get_system_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description is a noun phrase with no context, prerequisites, or exclusions, leaving the agent without any basis for selecting this tool over its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it merely labels the tool. It does not indicate whether the operation is read-only, what data it returns, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than conciseness. It is a noun phrase without a verb or any explanatory content, so it does not earn its place as a useful description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool is a status tool among many sibling status tools, the description is completely inadequate. It does not explain what system status includes, the return format, or how it differs from get_platform_status, wm_status, or sensor_status.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters and the schema confirms this (100% coverage). The description adds no parameter information, but none is needed since there are no parameters; the baseline for 0-param tools is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'ASTRA System Status' is essentially a restatement of the tool name 'get_system_status' without providing a verb or specifics about what status information is returned. It fails to differentiate from sibling tools like 'get_platform_status', 'wm_status', and 'np_status'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as 'get_platform_status' or 'wm_status'. No context, prerequisites, or scenarios are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description is just a title. It doesn't disclose whether the operation is read-only, what output to expect, whether it has side effects, or any other behavioral traits, leaving the agent with no information beyond the tool's 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?
While the description is extremely short, it is under-specified rather than concise. It is a fragment that provides no actionable information, so it fails to earn its place as a useful description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no schema, no output schema, and no annotations, the description is the only source of context. Its single phrase is insufficient to understand the tool's purpose, output, or relationship to the many sibling status tools.
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 accepts zero parameters, and the schema is empty. Since there are no parameters to document, the description doesn't need to add parameter semantics; the baseline for 0 params is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'NeuroPlatform v2 — Platform & Controller Status' is a noun phrase lacking a clear verb, so it doesn't explicitly state what the tool does (e.g., 'get' or 'retrieve'). It also doesn't distinguish itself from sibling tools like 'get_platform_status' or 'wm_status', which likely serve similar status-reporting roles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no context about when to use this tool versus alternatives. There are multiple status-related siblings (get_platform_status, get_system_status, wm_status), and no guidance is offered to choose among them.
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 exist, and the description discloses no behavioral traits: it doesn't say whether the tool mutates simulation state, how many steps it jumps, what happens to existing simulation data, or whether it returns any result. 'Advance' implies progression, but the nature and consequences are entirely unstated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than conciseness. It is a single vague phrase with no structure or contextual 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?
For a tool that likely mutates simulation state, with no annotations and no output schema, this description is completely inadequate. It doesn't explain side effects, prerequisites, or return values, and the sibling context suggests many related tools that could be confused with this one.
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 sole parameter 'steps' is well-constrained in the schema (integer, default 1, min 1, max 1000), but the description doesn't explain its relationship to the advance operation, and schema coverage is 0%. The description adds no meaning beyond 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 uses the verb 'Advance' with resource 'SNN Simulation', giving a basic sense of the tool's function, but it remains vague—no scope, timestep semantics, or distinction from sibling tools like simulation_control or snn_reset. It's not a tautology, but it 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 guidance is provided on when to use this tool, what conditions apply, or how it differs from alternatives. The description doesn't mention simulation state requirements or any 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 are provided, so the description carries full burden. It only states 'CEM Planning' with no disclosure of side effects, whether it modifies state, requires permissions, or has side effects. The description gives no behavioral context beyond the tool name itself.
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 a single noun phrase, which is compact but under-specified. It is not a complete sentence and provides minimal information, making it closer to under-specification than effective conciseness. It lacks any structural detail that would help an agent parse the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and only a terse description, the tool is grossly incomplete. There is no mention of return values, the planning process, how the plan is used, or any constraints. For a tool with nested objects and a complex domain (CEM planning), this description is far from sufficient.
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 adds no parameter information, but the schema already documents 'goal' (target state) and 'horizon' (planning steps). The nested structure of 'goal' is adequately described in the schema, so no gap exists.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'CEM Planning for Optimal Spike Injection' indicates the tool performs planning using the CEM method for spike injection, which is more specific than a tautology. However, it lacks an explicit verb like 'generate' or 'compute', and the resource ('spike injection') is somewhat clear. It distinguishes somewhat from sibling 'inject_spikes' (actual injection vs. planning) but 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 Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, when planning is appropriate, or how it compares to sibling tools like inject_spikes, wm_predict, or simulation_control. This is a complete absence of 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?
With no annotations provided, the description carries the full burden of behavioral disclosure, but 'Full State Snapshot' reveals almost nothing about what the tool actually does, what data it returns, whether it is destructive, or what side effects it might have. The phrase suggests comprehensiveness but lacks concrete behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief, but this is under-specification rather than efficiency. A single vague noun phrase fails to provide any usable structure or information. It does not meet the standard of 'every sentence earns its place' because the phrase adds almost no value beyond the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema, annotations, and detailed input schema, the description is the only source of context. For a tool that appears to export a full state snapshot, the description is completely inadequate—it does not specify what 'full state' includes, the format of the output, or any side effects. An agent would have no idea what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is no schema information to clarify. The rubric establishes a baseline of 4 for no-parameter tools, and the description does not need to explain parameter semantics. The minimal description does not detract from this baseline.
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 'Full State Snapshot' is a noun phrase that essentially restates the tool's name, 'export_snapshot'. It lacks a specific verb or explicit action, and it does not distinguish this tool from siblings that also inspect state (e.g., get_system_status, wm_status, get_metrics). The purpose is only vaguely implied.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as get_system_status or get_metrics. No context is provided for the intended use case, and the description does not mention any exclusions or alternative scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state what the tool returns, whether it has side effects, requires authorization, or is read-only. The phrase 'Consciousness Assessment (Proxy)' gives no insight into behavior, making this a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than concise clarity. A single vague phrase does not earn the tool's place, and it fails to provide any meaningful structuring or front-loaded 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?
Given the tool has no annotations, no output schema, and a single ambiguous phrase, the description is completely inadequate for an agent to understand what this tool does. It lacks even basic context about the returned score, units, range, or purpose within the larger system.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is trivially complete. According to the rubric, a baseline of 4 is appropriate when there are no parameters to document, and the description does not need to add parameter semantics.
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 'Consciousness Assessment (Proxy)' is a vague noun phrase that lacks a clear verb and resource specification. It does not explicitly state that the tool returns a score or performs an assessment, leaving the agent to infer from the tool name ('get_acm_score') and the context. This is more than a tautology but falls short of a clear purpose statement.
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 the many sibling tools. There is no mention of context, alternatives, or exclusions, so the agent has no information about the appropriate use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description must disclose behavioral traits, but it provides none. It does not say whether the tool is read-only, what state it returns, whether it has side effects, or what format the result takes. This is a complete lack of behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but brevity comes at the cost of essential information. It is under-specified rather than efficiently structured, as it omits any verb, scope, or usage context. This aligns with the calibration example where under-specification is penalized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations and no output schema, the description carries full responsibility for explaining the tool, but it fails to do so. It does not say what 'state' includes, how the result is presented, or how it relates to sibling state/status tools. For an agent to select and invoke this tool correctly, the description is severely incomplete.
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 defines zero parameters, so there are no parameter semantics requiring clarification. The description adds no parameter information, but with an empty schema this creates no gap. The zero-parameter baseline is met.
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 'SNN Engine State' is only a noun phrase that restates the tool name without an explicit verb or action. It identifies the resource but does not clearly state that the tool retrieves or returns this state, nor does it distinguish it from sibling status tools like wm_status or get_system_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use or alternative guidance is provided. The description is a standalone phrase with no mention of conditions under which get_snn_state should be chosen over related sibling tools such as get_system_status or get_metrics. This is a lack of guidance rather than misleading guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, and the description carries the full burden of behavioral disclosure. It only states 'Status' and reveals nothing about return format, side effects, read-only behavior, or failure modes, so the agent cannot predict tool behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than effective conciseness. The single phrase provides no actionable 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?
With no output schema and no annotations, the description should clarify what 'status' means and what the tool returns. It only offers a label, which is insufficient for an agent to know what to expect, especially given the large set of sibling status tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema places no burden. The description adds minimal domain context ('Multimodal Sensor Pipeline') but is not required to explain parameters that do not exist.
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 'Multimodal Sensor Pipeline Status' that restates the tool name without specifying an operation. It does not clearly distinguish this from sibling status tools like wm_status, np_status, or get_system_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as sensor_visual, sensor_audio, or get_system_status. The description lacks any context about the intended use case 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a mutation ('Modify') but does not disclose side effects, persistence, permission requirements, return behavior, or whether changes are reversible. This is a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only three words and reads as an incomplete fragment rather than a structured sentence. While it is technically concise, it is under-specified to the point of being unhelpful, so conciseness here is a negative trait rather than an asset.
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 parameter descriptions, the description is far too incomplete for an agent to understand the tool's role in the broader system. It provides only a high-level action and does not explain what 'state parameter' means, how path/value interact, or what the tool accomplishes in 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%, and the description does not explain the meaning of the 'path' or 'value' parameters. It adds no semantic value beyond the parameter names and types already visible in the input schema, leaving the agent to guess what values are valid or how path targets the parameter.
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 a clear action ('Modify') and a resource ('State Parameter'), but the resource is vague and could refer to any system state. It does not distinguish from sibling tools or specify which parameter context is involved, so it is more than a pure tautology but lacks meaningful 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?
There is no guidance about when to use this tool versus alternatives, no mention of prerequisites, and no indication of expected inputs or scenarios. The description only says 'Modify State Parameter,' providing no contextual or exclusionary information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure, but it only names a state and its components. It does not state whether the tool is read-only, whether it has side effects, what it returns, or what errors may occur.
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 short and front-loaded, but it is under-specified rather than efficiently concise. The colon-delimited list does convey some content, yet the lack of a verb makes it a fragment rather than a structured tool explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a state-related tool with no output schema, no annotations, and no action verb. The agent has no way to know what invoking this tool returns, when it is appropriate, or what behavior to expect, so the description is severely incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters and an empty input schema, so the baseline of 4 applies. The description does not need to explain parameter behavior because there are no parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description is a noun fragment ('Self-representation state: interoception, epistemic model, temporal continuity, attention schema') rather than an action. It mostly restates the tool name and lists subject areas, but does not state what operation the tool performs (e.g., get, update, log).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool or how it differs from sibling tools such as tcai_metaconsciousness or tcai_workspace_state. No alternatives, exclusions, or contextual triggers are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only offers a heading-like phrase. It does not state whether the operation is read-only, what data is returned, whether a running simulation is required, or any side effects, offering essentially no behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short and front-loaded, but it is under-specified rather than concise in a useful way. It is a fragment with no sentence structure or additional context, and it does not earn its place given the ambiguity it leaves unresolved.
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 a large sibling set with overlapping names (get_metrics, get_system_status, tcai_metrics, etc.), this description is insufficient to guide tool selection. There is no output schema or annotations to compensate, so the agent has very little information to choose correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema is trivially covered. The description cannot add parameter meaning beyond the empty schema, and the baseline of 4 applies. No parameter-related information is needed or missing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the resource ('World Model') and broadly mentions 'Status & Metrics,' but lacks a verb and specific scope. It does not distinguish the tool from siblings like get_metrics, get_system_status, or tcai_metrics, making it a vague phrase rather than a clear action statement.
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. It does not describe typical use cases, prerequisites, or exclusions, leaving the agent without direction for selection among many similar status/metrics tools.
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 whether the tool performs a capture (side effect) or retrieves an existing capture. It also lacks information about permissions, failure modes, or return behavior beyond a vague hint of 'descriptor + viability'.
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 short and front-loaded, but the brevity borders on under-specification. There is no wasted wording, but the cryptic phrasing 'Last MEA camera capture' and parenthetical output list are not sufficiently explanatory.
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 only one well-documented parameter, but no output schema and no annotations. The description offers only a vague output hint and fails to clarify the exact action, return values, or applicable scenarios, leaving important gaps for a simple but under-specified tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the 'mea' parameter with range and default, providing 100% coverage. The description adds no extra meaning about the parameter, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies an MEA camera capture operation, but the phrase 'Last MEA camera capture (descriptor + viability)' is ambiguous about whether it captures a new image or retrieves the most recent one. It does not clearly distinguish this tool from sibling np_* 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 is given about when to use this tool versus alternatives. There is no mention of prerequisites, 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 are provided, and the description gives no behavioral details such as state changes, autonomy, or computational cost. It only states the high-level operation, leaving the agent without information on side effects or 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 exceptionally concise, but it is a fragment that trades clarity for brevity. It contains only the core idea without supporting context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a multimodal pipeline with no output schema and no annotations, the description is far too sparse. It doesn't explain the role of each parameter or what 'Fused z' represents, leaving significant ambiguity for an 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 describes all 3 parameters with descriptions, covering 100% of them. The description adds no additional parameter semantics, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a multimodal fusion pipeline producing a fused output z, distinguishing it from individual sensor tools like sensor_visual and sensor_audio. However, it lacks an explicit verb (e.g., 'runs' or 'processes') and the phrase is more of a label than a functional statement.
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 over sensor_fuse or individual sensor tools. The 'Full Multimodal' label implies a comprehensive process, but no alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions the NeuroPlatform v2 DB context and the metric but does not clarify whether this is a read-only operation, what output format is returned, or how the spike rate is computed. This leaves significant ambiguity about side effects and return values.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the key concepts (DB, metric, time window) with no filler or redundancy. Every word contributes to the purpose.
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 description is too sparse for a query tool with no output schema. It does not explain the return structure, whether results are aggregated per electrode, or how nonzero_only affects results. Given the lack of annotations and output schema, the description is insufficient for an agent to fully understand the tool's behavior.
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 description adds no parameter-specific meaning. The schema already describes fsname and window_sec, but nonzero_only is undocumented in both the schema and the description. The description does not compensate for this gap, so it adds little value beyond the schema's partial coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource (spike counts per electrode) and the computation (spikes/minute over a time window). However, it lacks an explicit verb such as 'get' or 'list', and the purpose is phrased as a label rather than a full sentence, which slightly reduces clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus similar sibling tools like np_count_spikes or np_query_spike_events. The description does not mention any context, alternatives, or exclusions, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool returns individual spike timings (as opposed to aggregates) and references a time window, but it does not state that it is a read-only query, describe return format, or mention any side effects or limitations. This is insufficient for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly worded sentence that immediately communicates the domain and core function, with no filler or redundancy. It is appropriately concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, no annotations, and only 33% schema description coverage. The description provides a high-level summary but omits critical context such as expected return structure, relationship to sibling count tools, and behavior of parameters like limit and fsname. It is not complete enough for reliable tool selection and invocation.
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 only 33% (only window_sec has a description). The description adds little parameter-level meaning: 'over a window' aligns with window_sec but does not explain limit or fsname. Given the low coverage, the description should compensate but does not.
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 'SpikeEventQuery: individual spike timings over a window' clearly identifies the tool's function as retrieving individual spike events within a time window, distinguishing it from sibling count-based tools like np_count_spikes and np_query_spike_count. However, it lacks an explicit imperative verb (e.g., 'Query' or 'Retrieve'), reading more like a label than a directive.
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 np_query_spike_count or np_query_triggers. The description simply states what it is without contextualizing its use case relative to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does not state whether simulation is used, what the latent output represents, or any side effects. The high-level transformation arrow is insufficient to understand actual 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 extremely brief, making it concise, but it is a noun phrase rather than a well-structured sentence. It is front-loaded with the key concept but omits necessary details, so it is not fully effective.
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 complex nested input and absence of annotations and output schema, the description is grossly incomplete. It only supplies a title, lacking usage, behavioral, and parameter context, making it inadequate for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides a nested 'input' object with a description and typed properties with defaults, so schema coverage is high. The tool description adds no parameter semantics, but the baseline of 3 applies because the schema already documents the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Koniku Kore Olfactory Encoding (Chemoreceptor → Latent)' clearly identifies the tool's domain (olfactory encoding) and the transformation it performs, distinguishing it from sibling sensor tools like sensor_visual and sensor_audio. However, it lacks a clear verb and is phrased as a noun phrase rather than a complete statement of action.
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 or sensor_audio. The description does not mention prerequisites, scenarios, or exclusions, leaving the agent to infer usage solely from the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It mentions 'inertia' but does not explain what it means (e.g., temporal smoothing, statefulness) or whether the tool has side effects, requires prior state, or is a read-only operation. This is insufficient for an agent to understand the tool's behavior.
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, front-loaded sentence with no wasted words. It is efficient but could be slightly more structured to separate purpose from behavioral notes while still remaining 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 there is no output schema and no annotations, the description must carry significant context. It does not explain what PAD space means, what the output looks like, what 'inertia' implies, or any side effects. This is far from complete for a tool with 4 parameters and 0% schema coverage.
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 any of the 4 parameters (threat, novelty, rewardSignal, controllability). It only refers to 'raw signals' without mapping them to parameters or clarifying their meaning, ranges, or roles.
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 a specific verb ('Appraise') with a specific resource ('raw signals') and a specific outcome ('PAD emotional space (Mehrabian)'). This distinguishes it from siblings, as no other tool appears to handle emotion appraisal.
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 others. It does not mention context, prerequisites, or alternatives. The only implied usage is from the name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description does not disclose any behavioral details beyond the subject matter. It neither states that the operation is read-only nor describes any side effects, return format, or access-history implications. With no annotations to rely on, this 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise line with a colon-delimited list. It is easily scannable, though it is a fragment rather than a complete sentence, which slightly reduces structural 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?
The tool has no annotations and no output schema, yet the description does not explain what the agent should expect as a result. It lists state components but omits return format, invocation context, or how it relates to similar state tools. More contextual detail is needed for a tool in a complex workspace environment.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema coverage is trivially 100%. The description need not explain parameters, and it doesn't. Baseline of 4 for zero-param tools applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the subject (Global Neuronal Workspace state) and lists key components (ignition, focus, qualia, sync R, unity metrics, access history), but lacks a verb to clarify whether this retrieves, sets, or monitors state. It does distinguish somewhat from siblings that focus on other subsystem states, but remains vague about the exact action.
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 invoke this tool instead of alternatives like wm_status or get_metrics. The description does not mention any preconditions, typical scenarios, or exclusions relative to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry full transparency burden. It discloses a high-level pipeline but omits side effects, state changes, prerequisites, or safety implications. The mention of 'ethics' coupling hints at significant behavioral consequences without detail.
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 with no filler, efficiently covering the main workflow. It front-loads the product name and closed-loop concept, though the dense jargon makes it less immediately parseable.
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, and the description does not explain return values or expected outcomes. It also lacks prerequisites, configuration guidance, and relationships to sibling tools. For a 4-parameter orchestration tool, this is incomplete but not entirely absent.
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 (75%), so the baseline is 3. The description's 'optionally drive the SNN' loosely maps to drive_snn/drive_strength, but it does not add meaning beyond the schema. window_ms remains undocumented in both schema and description, but overall coverage is adequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the tool as a closed-loop pipeline: read organoid, couple to ASTRA fusion/ROS/ethics, and optionally drive the SNN. It has a specific verb chain and resource scope, though jargon-heavy and not clearly differentiated from sibling tools like sensor_fuse or inject_spikes.
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 versus alternatives. The phrase 'optionally drive the SNN' implies configurable behavior but does not state when this tool should be preferred over sibling tools like inject_spikes or check_ethics. Usage context is only vaguely implied.
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?
There are no annotations, so the description must disclose behavioral traits. It does not mention whether this is read-only, whether it triggers stimulation, what the return value looks like, or any side effects. The term 'Closed-loop' suggests potential interaction with stimulation, but no details are provided. This lack of disclosure 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no unnecessary words. It is front-loaded with the tool's purpose. However, it is somewhat cryptic due to the '_count_spike' phrasing and the dash/colon structure, which slightly reduces clarity. Still, it earns its place as a brief summary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description should provide more context about return values and distinguishing features. It only states the basic counting function, omitting any mention of output format, comparison with siblings, or operational context. The sibling 'np_query_spike_count' makes this lack of differentiation particularly problematic.
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 both parameters with descriptions (100% coverage). The description's 'N-ms window' loosely maps to 'window_ms' but adds no new detail beyond the schema. It does not clarify value formats, units, or interaction between top_k and window_ms. Baseline of 3 is appropriate since schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'spikes per electrode over an N-ms window', which clearly indicates the tool counts spikes per electrode within a time window. The action (counting) is implied by the verb in the tool name and the phrasing. However, it does not explicitly differentiate from the sibling tool 'np_query_spike_count', and the 'Closed-loop _count_spike' prefix is somewhat cryptic, leaving ambiguity about whether this is for closed-loop experiments only.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like 'np_query_spike_count' or 'np_query_spike_events'. It lacks any mention of scenarios, prerequisites, or exclusions. The only hint is 'Closed-loop', which implies a specific context but is not explained.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose side effects, but it only mentions 'execute uploaded StimParams' without clarifying whether triggers have lasting effects, need pre-uploaded params, or what happens on invalid input. It also inaccurately calls the 0-15 range a '16-bit trigger array'.
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 with no filler, front-loading the verb 'Fire'. It is concise but arguably too sparse, missing relationships to other NP tools.
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, no annotations, and two parameters, the description should explain prerequisites (uploaded StimParams), potential physical effects, and how this tool fits with np_configure_stim and np_query_triggers. It does none of this, leaving the agent without enough context to use it safely.
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?
Both parameters have schema descriptions, so baseline is 3. The description adds minimal value, only hinting that triggers map to a 16-bit array, but the schema already explains trigger keys and repeats. The 16-bit terminology is misleading since max is 15.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Fire' and identifies the resource 'trigger(s)' and the effect 'execute uploaded StimParams via a 16-bit trigger array'. This distinguishes it from query tools like np_query_triggers, though it doesn't explicitly name any sibling.
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 when-to-use or alternative guidance is given. The description only states the function, not when to prefer this over np_configure_stim or np_closed_loop.
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, but it only describes the encoding pipeline. It does not mention the simulate flag (which generates a simulated waveform), source selection, or any side effects. This leaves the agent without critical operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused phrase with zero redundancy or filler. It is front-loaded and easy to parse.
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 a detailed schema, the description lacks complete context: no explanation of when to use it, what the output latent represents, or how it fits into the sensor/A-JEPA workflow. The absence of annotations and output schema increases the burden, which the description does not meet.
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 adds no parameter-specific meaning, but the schema already provides defaults, ranges, and some descriptions (e.g., simulate, frequency), which sufficiently document 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 performs A-JEPA audio encoding, with a specific transformation pipeline (Waveform → Mel → Latent). This distinguishes it from sibling sensor tools (e.g., sensor_visual, sensor_olfactory), though it lacks an explicit action verb like 'captures' or 'converts'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description does not mention that it should be used for audio input, nor does it reference sibling tools or provide usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but 'Reset SNN Engine' is minimal. It does not mention what state is reset, side effects, reversibility, or any impact on the system, leaving the agent without critical information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief and to the point, but it is under-specified rather than efficiently concise. It lacks sufficient detail to be considered a well-structured explanation, though it is not bloated.
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 reset operation with no parameters and no output schema, the description should at least explain what 'SNN Engine' refers to and what resetting entails. The presence of tcai_reset creates ambiguity that is not resolved, making the description incomplete for agent decision-making.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the input schema fully defines the interface. The description adds no parameter semantics, but with no parameters to document, the schema baseline of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The verb 'Reset' and resource 'SNN Engine' clearly state the operation and target, but the description does not differentiate from the sibling tool tcai_reset, which may also perform a reset. It is clear but lacks sibling distinction.
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_reset. The description simply names the action without 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?
There are no annotations provided, so the description carries the full burden of behavioral disclosure. It mentions 'Predict' but does not clarify whether this operation is read-only, whether it affects system state, or any side effects. The description lacks important behavioral context such as mutability, auth needs, 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 a single, concise phrase that conveys the core purpose without unnecessary words. It is front-loaded and easy to parse. However, it may be overly sparse, lacking detail that could be valuable, but for purpose clarity it is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a nested action object and multiple parameters, indicating moderate complexity, yet the description is minimal. There is no output schema and no annotations, so the description should compensate by explaining latent space context, return values, and usage nuances. It fails to provide sufficient context for an agent to fully understand the tool's role and expected behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both 'steps' and 'action' having descriptions explaining their purpose. The tool description adds no additional parameter information, but the schema already documents the parameters thoroughly. Baseline 3 is appropriate as the structured fields carry the semantic weight.
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 states the action (predict) and resource (SNN state in latent space), making the tool's purpose understandable. However, it does not explicitly distinguish this from sibling tools like wm_plan or wm_encode, which limits 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. It only states what the tool does, without any context about appropriate scenarios, prerequisites, or exclusions. This is a single declarative phrase with no usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It does not explicitly state that this is a read-only operation, nor does it describe what the returned data looks like, any side effects, or ordering/pagination behavior. The 'TriggersQuery' name implies a query, but the description adds no explicit transparency about safety or output 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, front-loaded with the tool name and core function, and contains zero filler. It efficiently conveys the essential purpose without unnecessary verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with two fully described parameters and no output schema. The description conveys the core function but lacks details about the return format or any usage caveats. Given the absence of annotations and output schema, more context about what the query returns would improve completeness, but the basic purpose is adequately covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides full descriptions for both parameters (dedup and window_sec), covering 100% of the parameters. The tool description adds no additional parameter semantics beyond what the schema already states. Baseline score of 3 is appropriate since schema description coverage is high and no extra value is added by the description.
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 'TriggersQuery: triggers sent to the organoid over a window', which clearly identifies a query operation for trigger data with a temporal scope. This distinguishes it from siblings like np_send_trigger (send) and np_query_spike_count/events (spike-related). However, it does not explicitly contrast with sibling tools, so a small deduction applies.
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 only states what it does, not when it should be chosen over similar query tools like np_query_spike_events or np_count_spikes. There are no exclusions or alternative tool mentions, leaving usage context entirely implicit.
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 for transparency. It mentions 'Optionally inject an RPE sample' but does not disclose whether this mutates internal state, whether the operation is reversible, or any side effects. Partial credit is given for the state interpretation, but critical behavioral 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact at two sentences and front-loads the core purpose. Technical jargon ('RPE-variance dynamics') reduces readability, but there is no fluff and every clause contributes meaning.
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 and no annotations, so the description must explain return values and side effects. It does not specify what exactly is returned (e.g., a status object, both velocity and noveltySpike), nor the effect of an injected RPE sample on the returned state. This is a significant gap for a dual read/inject tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the 'rpe' parameter is already described in the schema with range constraints. The description's mention of 'inject an RPE sample' adds no new detail, so it does not improve on the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific resource ('MetaLearningModule port') and its key outputs ('learning velocity', 'noveltySpike'), which distinguishes it from sibling tools like tcai_curiosity or tcai_convergence. However, it lacks an explicit verb (e.g., 'get', 'inject'), so the primary action is ambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it (inspecting meta-learning state) and gives interpretation of state values ('velocity>0 ⇒ converging; noveltySpike ⇒ novel/confusing regime'), but it does not explicitly state when to use this over alternatives or mention any 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 must fully disclose behavioral traits. It mentions 'validate & upload' but does not explain behaviors such as what happens on validation failure, whether the upload persists or overwrites existing configurations, or if any side effects occur. This leaves significant ambiguity for a configuration tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the platform (NeuroPlatform v2) and core action (Define, validate & upload). It contains no filler and conveys the essential purpose 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?
This is a complex tool with 11 parameters, no annotations, and no output schema. The one-sentence description does not cover important contextual aspects such as return values, device prerequisites, or behavior after upload. While the schema helps, the description itself is insufficient for an agent to fully understand the tool's operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 82%, so the schema already documents most parameters (e.g., phase durations, amplitudes, charge-balance enforcement). The description adds minimal parameter context beyond the phrase 'charge-balanced biphasic stimulation', which is related but does not explain individual parameters or their interactions.
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 identifies the tool's function: 'Define, validate & upload a StimParam'. It uses specific verbs (define, validate, upload) and a specific resource (StimParam) with additional context (charge-balanced biphasic stimulation), distinguishing it from sibling tools like np_send_trigger or np_query_spike_count.
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 usage guidance is provided. The description does not mention when to use this tool versus alternatives, nor does it name any sibling tools or prerequisites. It only states what the tool does, leaving the agent to infer usage from the tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavioral traits. The description only lists metrics and the word 'report', but does not explain whether this is a read-only operation, what the return format is, whether any state changes occur, or if there are performance or rate-limit implications. This is a significant gap for a tool that might offer a detailed consciousness report.
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, highly concise line that front-loads the core purpose and lists key output components. Every word contributes: 'Consciousness proxy report' is the noun phrase, and the colon introduces the metrics list. There is no wasted text, and it is appropriately sized for a tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no input schema, no annotations, and no output schema, so the description must carry the burden of explaining both behavior and return values. It does identify the metrics that will be reported, which is helpful, but it does not describe the format, structure, or semantics of the report (e.g., how the composite score is derived, units, or how to interpret the values). For a read-only metrics tool, this is borderline adequate but leaves unanswered questions.
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 baseline is 4. The description does not need to explain parameters, and it adds value by indicating what the output will contain (specific metrics and a composite score). This is sufficient for a no-input tool.
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 a specific verb+resource: it is a 'Consciousness proxy report' that includes GNW metrics, Effective Information, Φ̃-RIIU, and a composite score. This clearly identifies the tool's function and distinguishes it from siblings like get_metrics or get_acm_score by enumerating the exact metrics it reports. However, the verb is implied ('report') rather than explicit, and the phrase 'Consciousness proxy' may be somewhat abstract.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. It does not mention when it should be preferred over get_metrics, tcai_meta_learning, or other sibling tools, nor does it indicate prerequisites or context. The description provides no usage context beyond the tool's existence.
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 computes developmental stage, stability, and meta-representation scores over time, but does not state whether it is read-only, what return format is expected, or any side effects. This is adequate for a high-level understanding but lacks operational detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and front-loads the core purpose. It is dense with meaningful terms, though the heavy jargon (composite-proxy level, meta-representation) may obscure clarity for some agents.
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 zero-parameter tool, the description provides the essential output concepts but does not elaborate on return structure or the meaning of 'composite-proxy level'. Given the complexity and lack of an output schema, additional detail would help an agent interpret and use the result 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?
The tool has zero parameters, so the schema is trivially complete. The description does not need to explain parameters and does not introduce any, which 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 identifies the tool as longitudinal developmental tracking, listing a specific stage progression (nascent→reactive→integrative→reflective) and output metrics (stability, meta-representation score). It distinguishes itself from siblings by emphasizing temporal tracking, though it lacks a strong action verb and relies on 'DevelopmentTracker port' for context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like tcai_second_order or tcai_self_model. It does not state conditions for use, prerequisites, or exclusions, leaving the agent to infer appropriate invocation contexts.
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 of behavioral disclosure. The word 'snapshot' strongly suggests a non-mutating read operation, and 'observing' reinforces this. However, it does not explicitly rule out side effects, state changes, or prerequisites, nor does it explain what 'self-evidencing' means behaviorally. The description adds some context (the system observing/correcting its predictive capacity) but lacks explicit safety or side-effect declarations.
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 two sentences. It front-loads the tool's purpose ('Second-order loop snapshot') and then lists the contained elements. The second sentence adds conceptual context, though the citation ('Legros 2026 §3.2') may be of limited value to an AI agent. Every word earns its place, but the dense jargon might reduce immediate readability.
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 has no input schema parameters and no output schema, the description must explain both purpose and return semantics. It successfully lists the snapshot components but does not describe the output format (e.g., JSON shape, numeric ranges, or units). It also omits prerequisites or whether this is a lightweight read or a costly computation. The composite nature justifies a higher complexity, so the description is adequate but not complete.
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 baseline for this dimension is 4. The description adds no parameter information because none exists, and the schema is trivially complete (100% coverage). There is no need for compensation; the baseline applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a 'second-order (self-evidencing) loop snapshot' and enumerates the specific state components it captures (meta-learning velocity, RND curiosity, capability model, meta-consciousness score, developmental stage). This distinguishes it from more focused siblings like tcai_meta_learning or tcai_curiosity by its composite nature, though the absence of an explicit verb (e.g., 'get', 'retrieve') and reliance on jargon slightly reduce clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is given about when to use this tool versus alternatives. While the description implies it provides a high-level overview of multiple self-monitoring aspects, it does not state conditions, exclusions, or mention sibling tools that might be more appropriate for specific details (e.g., 'use tcai_meta_learning for in-depth meta-learning metrics'). The usage context is only implicitly conveyed.
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 bears full responsibility for behavioral disclosure. It explicitly states the output is a weighted score and warns that it is a proxy, not a measurement. However, it does not describe the output format, scale, or explicitly confirm that the operation has no 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, composed of two sentences with minimal waste. The parenthetical '(MetaconsciousnessEvaluator port)' adds provenance but is slightly tangential. Overall, it is efficiently organized and front-loaded with the tool's purpose.
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 low complexity (0 parameters, no output schema), the description is largely complete. It explains the composite nature, lists the components involved, and provides an important caveat about its proxy status. It could specify the output scale, but this is not critical for an agent to understand the tool's function.
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 baseline score is 4. The description does not need to add parameter-level semantics, and none are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides a 'Meta-consciousness composite' as a 'weighted score over confidence calibration, learning awareness, self-continuity and error monitoring.' It clearly identifies the resource and nature of the output, though it lacks an explicit verb like 'computes' and does not distinguish itself from similar sibling tools like tcai_metrics or tcai_self_model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus its siblings. The description does not mention alternatives or conditions for use. The caveat 'PROXY of meta-representation capacity, not a measurement' is interpretational rather than tool-selection guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits itself, but it only states the operation without indicating whether it is read-only, modifies state, or has side effects. It also fails to mention what happens to the SNN state or whether the encoding is reversible or destructive, which is a notable gap for a transformation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, succinct sentence that is fully front-loaded and free of redundancy. Every word contributes to the core meaning, making it an appropriately concise description for a tool with no parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema and no annotations, so the description should explain return values or behavioral outcomes, but it does not. It also lacks any mention of when this encoding is useful or what the latent space represents, leaving the description incomplete for practical use despite its simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is empty with 100% coverage vacuously. The baseline for 0 parameters is 4, and the description does not need to compensate for parameter documentation since 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?
The description uses a specific verb 'Encode' with a clear resource 'SNN State' and target 'Latent Space', making the tool's function immediately understandable. It also distinguishes itself from sibling tools like wm_predict and wm_plan by focusing on encoding rather than prediction or planning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as wm_predict, wm_train_step, or wm_surprise. The description gives no context about prerequisites, typical scenarios, or exclusions, leaving the agent to infer usage solely from the name.
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 like return type, side effects, or state requirements. It discusses the conceptual algorithm and default behavior but fails to state whether this is a read-only computation, whether the online predictor is updated, what the output looks like, or any edge-case behavior. This is a significant gap for an unannotated tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with the core purpose, each sentence provides distinct value: algorithm, interpretation, and default behavior. No filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is conceptually explained (RNDCuriosity, novelty, EFE proxy) and the default input is stated, which is helpful. However, with no output schema, the description omits the return value format (likely a scalar but unstated) and any side effects or dependency on internal state (e.g., whether the online predictor is updated), making it incomplete for operational 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?
The schema already fully describes the single 'embedding' parameter ('Representation vector (defaults to current broadcast)'), and the description echoes this default. The description adds a bit of conceptual context (prediction error on representation vector) but doesn't alter parameter meaning, so the baseline score of 3 applies given 100% 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?
The description clearly states the tool computes intrinsic reward/curiosity via RNDCuriosity prediction error, using the term 'RNDCuriosity port' and explaining the meaning (high error = novelty). This distinguishes it from sibling tools like tcai_active_inference or tcai_meta_learning, which have different purposes.
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 by saying it 'Defaults to the current GNW broadcast', indicating a common no-argument invocation, but it does not explicitly state when to prefer this tool over alternatives or any exclusions. No alternative tools are mentioned.
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 full responsibility for behavioral disclosure. It does list the output quantities and states that it is 'core telemetry,' which suggests a read-only operation. However, it does not explicitly confirm read-only behavior, potential side effects, computational cost, or error conditions, leaving some ambiguity.
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 dense sentence packed with technical details and citations. While it is front-loaded with the main purpose, the emphasis on 'REAL,' version number, and the citation to Da Costa et al. 2020 and Legros 2026 add length without significantly aiding understanding. It is slightly overlong for a telemetry getter.
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 complex telemetry tool with no output schema, the description names all key outputs but does not explain their format, units, or typical usage patterns. It gives a high-level overview and references the halting criterion, but an agent would still need to infer how to interpret the values. The description is moderately complete.
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 is not required to explain parameter syntax. It compensates by describing the output fields in detail, which is helpful given the absence of an output schema. The baseline for zero-parameter tools is 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 identifies the tool as providing active-inference core telemetry, enumerating specific quantities (variational free energy F, expected free energy G(π), task quality, model entropy, Dirichlet-learned action). While it lacks an explicit verb like 'returns' or 'gets', the noun 'telemetry' implies a data retrieval function, and the specificity distinguishes it 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage context by stating 'This is the principled quantity the halting criterion thresholds on' and notes 'not a heuristic correlate.' This tells the agent when to use this tool (for principled halting decisions) and implicitly warns against substituting heuristic metrics. However, it does not name specific sibling alternatives, so the guidance is moderately explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the burden. It discloses the processing pipeline and halt behavior, but does not state side effects (e.g., updates to memory/self-model), prerequisites, or whether repeated calls accumulate state. It implies state changes via the pipeline but not explicitly.
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 purpose and pipeline, followed by the key stop-condition behavior. No wasted words; every clause adds value.
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 13 parameters and no output schema, the description lacks return-value guidance, state-change expectations, and definitions for several parameters. An agent would struggle to invoke it correctly beyond the basic run-and-halt pattern.
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 (~77%) and the schema already documents most parameters. The description adds meaning around stopWhenSatisfied and the 'sustained satisfactory' halt regime, but doesn't add much for the remaining parameters like threat, controllability, predictionError, and predictionConfidence.
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 uses specific verb 'Run' with resource 'ACM cycles' and details the pipeline from SNN signals to second-order loop, clearly distinguishing it from sibling tools like tcai_second_order or snn_step. It states what the tool does at the right level of specificity.
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?
Description gives clear context: run one or more ACM cycles, with explicit guidance on setting stopWhenSatisfied for early halt. It doesn't name alternatives or when-not-to-use, but the usage context is clear enough for most cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It discloses the algorithmic basis (blended cosine similarity, PAD congruence, salience), but does not mention whether the operation has side effects, what the return format is, or how the blending weights are determined. This is a moderate level of 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?
The description is a single, front-loaded sentence that efficiently conveys the core purpose and method without unnecessary 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?
Given the tool's complexity (5 optional parameters, no output schema, no annotations), the description provides a high-level method but lacks return value expectations, usage preconditions, or guidance on parameter combinations. It is minimally sufficient for understanding the tool's existence but not for confident invocation.
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% (topK lacks description). The description adds meaning to the PAD parameters by referencing 'PAD congruence', and mentions 'embedding' indirectly via cosine similarity. It does not elaborate on topK or embedding behavior beyond the schema's description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Retrieve' with the resource 'memories' and specifies the retrieval method (blended cosine similarity, PAD congruence, salience). This clearly distinguishes it from sibling tools like tcai_memory_store, which handles storing memories.
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 does not explicitly state when to use this tool vs alternatives. There are no references to other tools or exclusion criteria. The usage is implied by the tool's name and description (when you need to retrieve memories), but no clear guidelines are provided.
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 available, so the description carries the full burden of behavioral disclosure. It does reveal two behavioral traits—'attention-gated' and 'salience-indexed'—which add meaningful context beyond the simple 'store' operation. However, it does not discuss persistence, overwrite behavior, side effects, or prerequisites. This is partial disclosure but not a contradiction.
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, well-structured sentence that front-loads the primary action and then adds three useful qualifiers (emotional, attention-gated, salience-indexed). Every word earns its place, with no redundancy or filler. It is concise yet informative.
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 six parameters and lack of annotations or output schema, the description provides a high-level purpose but omits critical usage context. It does not explain return values or side effects, and offers no guidance on when to use this tool over the many memory-related siblings. The schema covers parameter details, but the overall description leaves gaps in usage guidelines and behavioral expectations, making it minimally adequate yet incomplete.
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 provides detailed descriptions for 83% of the parameters (all except attentionLevel), so the baseline is 3. The description adds value by framing the tool as 'attention-gated' and 'salience-indexed', which directly informs the likely role of the attentionLevel parameter and the emotional dimensions (arousal, valence, dominance) in indexing. This goes beyond the schema's bare range definitions and helps the agent understand how the parameters interact.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies both the verb ('Store') and the resource ('an experience in emotional memory'), making the tool's function immediately obvious. The parenthetical qualifiers 'attention-gated, salience-indexed' distinguish it from sibling tools like tcai_memory_retrieve and wm_encode by emphasizing its unique emotional memory focus and processing traits.
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 its usage for storing emotionally significant experiences but does not explicitly state when to use it over alternatives. No alternative tools are mentioned, and there is no exclusion criteria or when-not-to-use guidance. The phrase 'emotional memory' hints at a specific domain but leaves the agent to infer the selection boundary.
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 must cover behavioral traits. It specifies the affected components (workspace, memory, emotion, metrics), but does not disclose consequences, reversibility, or side effects such as data loss. It adds some scope context but does not fully carry the transparency burden.
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, ten words, no wasted text. Front-loaded with the verb and resource, with a parenthetical list of affected components.
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?
For a simple 0-parameter reset operation, the description is adequate, specifying what it resets. It could be enriched with side-effect information or relationship to sibling reset tools, but given the tool's simplicity, it is reasonably complete.
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?
Tool has zero parameters; schema coverage is vacuous. Baseline for 0 params is 4, so the description need not explain parameters, and no additional parameter semantics are required.
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 verb 'Reset' and the resource 'TCAI consciousness system', listing sub-components (workspace, memory, emotion, metrics). It distinguishes from sibling snn_reset which resets the SNN, making the purpose specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
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 over alternatives. The description implies usage when needing to reset the TCAI system, but does not mention exclusions or alternative tools like snn_reset.
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?
With no annotations, the description carries the full burden. It establishes read-only semantics via 'query' and 'list', and adds useful context about the underlying model (DirectExperienceLearner port, EMA). It does not detail edge cases or exact output format, but for a read-only lookup this is reasonably transparent.
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 is front-loaded with the essential purpose ('Agency capability model') and immediately explains the action-to-valence mapping. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with only one optional parameter and no output schema. The description covers the two primary operations (query by action, list all) and the conceptual model. It could mention return value details or behavior for unknown actions, but overall it is sufficient for basic invocation.
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 already fully describes the 'action' parameter, so the baseline is 3. The description adds meaning by explaining that the parameter is optional and that omitting it triggers the list-table behavior ('or list the learned capability table'), which is not stated in the schema. This goes beyond 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 uses specific verbs ('query', 'list') and identifies the resource ('capability model', 'action → expected-valence map'). It clearly distinguishes this as a lookup tool for learned action-outcome associations and distinguishes it from siblings like tcai_meta_learning or tcai_second_order.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: to query an action's expected outcome or to list the capability table. However, it does not explicitly mention alternatives or provide exclusions relative to other tcai tools, so usage guidance is present but not fully explicit.
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?
With no annotations, the description carries the full burden. It explains the AND condition and the insufficiency of stationarity, which are important behavioral details. However, it does not disclose whether updates persist or affect running loops, so it falls short of full 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?
Two sentences, front-loaded with the action, and the second sentence efficiently packs the halting logic. The citation is somewhat extraneous but does not bloat; every clause serves a purpose.
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?
The description covers both read and write modes, explains the functional condition, and notes the return content for the no-args case despite lacking an output schema. It is sufficiently complete for a configuration tool of this complexity.
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%, so baseline is 3. The description adds value by linking parameters into a cohesive halting condition (|ΔF| ≤ epsFreeEnergy, ≥ minTaskQuality, patience cycles), but it does not explicitly mention maxEpistemic by name, relying on 'epistemic value is low'.
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 identifies the tool as inspecting or configuring the halting criterion, a specific resource distinct from sibling tools like tcai_second_order or tcai_active_inference. The verb 'inspect or configure' is specific, and the resource is unambiguously named.
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?
It explicitly states behavior with no arguments (returns state/thresholds) vs with arguments (updates them), giving clear context. It does not mention alternative tools or when not to use it, but the dual-mode usage is clearly laid out.
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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