ASTRA MCP Server

Server Quality Checklist

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Latest release: v2.9.0
Disambiguation2/5
Many tools have similar names and overlapping purposes, especially the 'tcai_' series (e.g., tcai_active_inference, tcai_convergence, tcai_curiosity) and 'np_' tools. Detailed descriptions help, but the high number of specialized tools makes it hard for an agent to disambiguate without deep domain knowledge.
Naming Consistency4/5
Most tools use a consistent snake_case pattern with domain prefixes (e.g., np_, tcai_, sensor_, wm_). Exceptions like 'inject_spikes' break the prefix pattern, but overall the naming is predictable and reasonably consistent across the set.
Tool Count2/5
50 tools is excessive for an MCP server. While the server covers a complex domain, such a large surface area likely overwhelms agents and indicates poor scoping. Many tools could be consolidated.
Completeness4/5
The tool set covers a wide range of necessary operations: system status, neuroplatform control, sensor pipelines, world model, and consciousness metrics. There are minor gaps (e.g., no explicit tool to delete memories or update sensor configs), but overall it is quite comprehensive for the stated purpose.
Average 2.7/5 across 50 of 50 tools scored. Lowest: 1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

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

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

How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations and a minimal description, the tool's behavioral traits are completely undisclosed. For a mutation tool, it fails to mention side effects, permissions, or reversibility.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While extremely short (two words), this under-specification is not conciseness but omission. Every sentence should earn its place, and here no useful information is conveyed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (2 required params, no output schema, many siblings), the description is wholly inadequate. It fails to provide any contextual completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to the parameters 'path' and 'value'. The agent must guess what these represent, making correct invocation unlikely.
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 'Modify State Parameter' is essentially a tautology of the tool name 'set_parameter'. It provides no specific verb or resource that distinguishes this tool from its many siblings, such as get_snn_state or np_send_trigger.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. Lacking any context such as prerequisites, exclusions, or examples, the agent has no clue 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?
With no annotations, the description carries full burden for behavioral disclosure, but it provides none. The agent cannot infer effects like state changes, idempotency, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely short, but this is under-specification rather than concise. Every sentence should be useful; this single sentence is entirely redundant with 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 presence of many specific sibling tools and no output schema, the description is critically incomplete. An agent cannot determine what simulation_control affects or how it relates to other tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to the 'command' parameter. Although the schema provides an enum, the description offers no interpretation of what each command does.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Simulation Control' is a tautology that merely restates the tool name. It does not specify any verb or resource, making it impossible for an agent to discern the tool's purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus its many siblings. The description offers no context for appropriate invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist and the description offers no behavioral details. It is not misleading but entirely insufficient for understanding side effects or requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely short but not usefully concise; it lacks essential information. Every word should add value, but this phrase is too vague.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, many siblings, no output schema), the description is severely incomplete. It fails to explain fusion behavior, return values, or relationship to other sensors.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description adds no meaning to the three boolean parameters. It does not explain that they control which modalities to include.
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 'Cross-Modal Attention Fusion' is a vague phrase, not a verb+resource statement. It does not specify what the tool does or how it differs from siblings like sensor_process.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines provided. The description does not indicate when to use this tool versus alternatives such as sensor_visual or sensor_audio.
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 description offers no behavioral details (e.g., read vs. write, latency, output format). Fails to compensate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely short, but this is under-specification, not conciseness. Lacks essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, no annotations, and a cryptic description, the tool is completely opaque. Cannot be used effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, so schema coverage is 100%, but description adds no value beyond the schema. It does not clarify what the tool returns or any context for its zero 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?
Description 'Bio-Computing Platforms' is vague and does not specify the action (e.g., 'gets platform status'). It fails to distinguish from sibling tools like get_system_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this tool versus alternatives. Missing entirely.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description gives no behavioral information. It fails to disclose effects such as mutation of simulation state, required permissions, or potential side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only two words, which is under-specified. It sacrifices informational content for brevity, making it insufficient for agent understanding.
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 two parameters and no output schema, the description should explain the tool's role within the neural simulation system. It fails to provide any context, leaving the agent with no understanding of what the tool accomplishes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no parameter details. The agent only sees parameter names and types, requiring inference for meaning.
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 'Spike Injection' is a nominalization of the tool name, providing minimal clarity. It vaguely suggests injecting spikes but does not specify target system or context, failing to distinguish from sibling tools like np_send_trigger.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like np_send_trigger or snn_step. The description offers no context for appropriate usage 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?
The description provides no behavioral traits such as whether it reads or modifies state, what it returns, or side effects. With no annotations to rely on, the description carries full burden and fails entirely.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but under-specified. It does not earn its place as it provides no useful information beyond the 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 no output schema, no annotations, and a single vague word, the description is completely inadequate for an agent to understand its purpose or behavior. It leaves all questions unanswered.
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 tool has zero parameters and schema coverage is trivially 100%. No parameter information is needed, but the description adds no context about what the tool checks without inputs. Baseline score applies due to zero-parameter nature.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'IRB Neural Welfare Check' is a vague noun phrase without a verb. It fails to specify what action the tool performs (e.g., check, verify, report) or what resource it acts on. Among many sibling tools with similarly cryptic names, this does not distinguish itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like get_metrics, get_system_status, or tcai_emotion_appraise. There is no mention of prerequisites 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?
No annotations are provided, and the description gives no behavioral details such as safety, idempotency, or side effects. The description fails to convey any behavioral traits beyond the tool name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but severely under-specified. It does not earn its place; it reads more like a title than a functional 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 complexity of the tool (multiple nested parameters, no output schema), the description is completely inadequate. It does not explain return values, behavior, or usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 100% (one parameter object), the description adds no meaning beyond the schema. The nested parameters lack descriptions, and the tool description does not clarify their usage or 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 'Koniku Kore Olfactory Encoding (Chemoreceptor → Latent)' is vague and does not clearly state what the tool does. It implies encoding but lacks a specific verb or resource; it barely distinguishes from siblings like sensor_audio or sensor_visual.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It lacks context about its purpose, prerequisites, or exclusion 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 present, and description does not disclose any behavioral traits (e.g., destructive, idempotent, required permissions).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely short but not concise; lacks substance and fails to earn its place with meaningful 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 no output schema and nested object parameters, description is woefully incomplete. Does not explain return values, side effects, or planning process.
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 provides full descriptions for both parameters. Description adds no extra meaning, but baseline is acceptable.
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 uses jargon 'CEM Planning for Optimal Spike Injection' without explaining CEM. Fails to differentiate from sibling tools like wm_predict or wm_encode.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. No context 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 given, and the description fails to disclose any behavioral traits (e.g., read-only, destructive, rate limits). The agent cannot infer side effects or safety requirements.
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 (two words), providing insufficient information. It sacrifices clarity for brevity.
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 no parameters and no output schema, the description must compensate but fails entirely. It lacks any explanation of what the tool returns or how it behaves.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so the schema is fully covered. The baseline of 4 applies as no additional parameter information is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Consciousness Assessment (Proxy)' is vague and does not specify what ACM stands for or what exactly the tool assesses. It provides minimal differentiation from sibling tools like tcai_metaconsciousness.
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 offers no guidance on when to use this tool versus alternatives. No context about prerequisites or constraints is 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 provided, so description must disclose behavior. It fails to mention side effects (e.g., file saving, data return), permissions, or output format. 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?
Extremely brief (3 words) but under-specified. Conciseness is not achieved because critical context is missing, making it inefficient for the agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no inputs or output schema, the description should explain the export purpose and result. It does not, leaving the agent without adequate information 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?
No parameters exist, and schema coverage is 100% trivially. Description adds no parameter info, but baseline is 3 as nothing is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Full State Snapshot' vaguely implies exporting the current state, but lacks specific verb and resource. It is not a tautology but provides minimal clarity beyond the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings like get_system_status or simulation_control. No context on prerequisites or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description does not disclose behavioral traits such as read-only nature, side effects, or permissions. While the name implies a getter, this is not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short (3 words), which is concise but under-specified. It lacks structure and may be too terse to be helpful.
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 parameters, no output schema, and no annotations, the description provides almost no context. It fails to explain what the state includes, its format, or how it relates to other 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?
There are zero parameters, and schema coverage is 100% by default. The baseline for zero parameters is 4; no additional parameter explanation is needed, though the description could still clarify what 'state' means.
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' barely adds value beyond the tool name 'get_snn_state'. It lacks specificity about what aspect of the SNN state is returned, making it a near-tautology.
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 over siblings like get_metrics, get_platform_status, or snn_step. The agent receives no decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does not state whether this is a read-only operation (retrieving the last capture) or a mutable action (triggering a new capture). The terms 'capture', 'descriptor', and 'viability' are undefined, leaving the agent uncertain about side effects or data nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, making it concise, but it is under-informative. It uses front-loaded terms but does not earn its place by providing sufficient clarity. The brevity reduces its helpfulness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description should explain what the tool returns. Terms like 'descriptor + viability' are not elaborated. The agent cannot infer the output format or contents (e.g., image, numeric data). The tool is simple but still incomplete for reliable invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes the 'mea' parameter as 'MEA selector [1-5]' with defaults and constraints. The description adds no extra semantic meaning beyond the schema, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Last MEA camera capture (descriptor + viability)' is vague. It does not clearly state a verb+resource combination. The meaning of 'last' is ambiguous—whether it retrieves the most recent capture or performs a new capture. It distinguishes minimally from siblings but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs. alternatives like sensor_visual or other camera-related tools. The description provides no context about prerequisites, typical scenarios, or scenarios where this tool is recommended or not.
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 alone must disclose behavioral traits. It does not mention side effects, state mutations, destructive actions, or return values. This severely lacks transparency for a tool that likely modifies simulation 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 a single sentence, but it is under-specified. Conciseness should not sacrifice informativeness. Every sentence must earn its place; here, the sentence is too minimal to be useful.
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 low complexity (1 optional param, no output schema, no annotations), the description still fails to provide enough context. The effect of the tool, its relationship to other simulation tools, and expected outcomes are not described.
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 coverage is 0%; the description does not mention the 'steps' parameter at all. While the schema provides min/max/default, the description adds no meaning or clarification about how the parameter affects the simulation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Advance SNN Simulation' provides a verb and resource, but 'advance' is vague. The parameter 'steps' clarifies that it progresses by a number of steps, but the description doesn't mention this. Purpose is somewhat clear but lacks specificity, leading to ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings like 'simulation_control' or 'snn_reset'. The description provides no context for appropriate usage, exclusions, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavior. It only states 'Platform & Controller Status' without detailing effects, safety, or output expectations. It does not clarify if it is read-only or what exactly is queried.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief but under-specified. It does not earn its place as it lacks essential information; conciseness is not achieved when critical details are omitted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, no output schema, and no annotations, the description should compensate by explaining what status includes. It fails to provide a complete picture, leaving the agent uninformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist (0 params, schema 100% coverage trivial). Baseline is 4 per instructions, and the description adds minimal context but does not contradict schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description 'NeuroPlatform v2 — Platform & Controller Status' is vague as it lacks a verb and does not clearly indicate the action (e.g., retrieve status). It does not distinguish from sibling tools like get_platform_status or get_system_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as get_platform_status or get_system_status. The description provides no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden but only hints at real-time data. No mention of side effects, data freshness, or access requirements for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at two words, but it sacrifices necessary detail. It is not overly verbose, but it does not earn its place with meaningful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and many sibling tools, the description is inadequate. It does not explain what metrics are returned, in what format, or how to interpret them.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the schema provides full coverage. The description adds no value beyond stating 'real-time', but baseline of 4 is appropriate for zero 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 'Real-time Metrics' is vague and does not specify what metrics are retrieved or how they relate to the tool's name. It fails to distinguish from siblings like 'get_acm_score' or 'tcai_metrics'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus others. There are no conditions, prerequisites, or examples of appropriate 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 must carry the burden of behavioral disclosure. It does not state whether the tool is read-only, requires permissions, or has any side effects, leaving the agent uninformed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, but this conciseness sacrifices necessary information. It reads more as a label than a helpful explanation, earning a middle score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should explain what the status includes or the return format. It fails to provide any such context, leaving the agent to guess the output structure.
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 already fully describes the input. The description adds nothing, but per guidelines, 0 params warrants a baseline of 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 tautology of the tool name 'get_system_status'. It lacks a verb and does not specify the scope or resource, nor does it differentiate from sibling tools like 'get_platform_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 on when to use this tool versus alternatives such as 'get_platform_status', 'sensor_status', or 'np_status'. The description provides no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It only names attributes but does not disclose side effects, idempotency, or return format. The agent cannot infer whether this is a read or mutation operation.
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 line with no unnecessary words, but it sacrifices clarity for brevity. It is front-loaded with the phrase 'Self-representation state' but omits a verb.
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?
Tool has no parameters and no output schema, so the description must cover basic usage and return behavior. It only lists abstract concepts, leaving the agent uncertain about what the tool actually provides.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has zero parameters, and schema coverage is 100%. Baseline is 4, and the description adds no parameter details since none 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 lists conceptual domains (interoception, epistemic model) but lacks a clear verb indicating the tool's action (e.g., retrieve, query, update). It is vague and does not specify what the tool does with these aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus the many sibling tcai_* tools. The description does not mention prerequisites, alternatives, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only says 'training step', not whether it modifies model state, is safe, or has side effects. The action parameter hints at neuro-inspired injection, but behavior is not explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (one sentence), but this brevity omits critical information. It is not that conciseness is achieved; rather, it is under-specified.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the nested parameter object and lack of output schema or annotations, the description is grossly incomplete. It does not explain return values, training process, or side effects, leaving the agent without essential 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 has 100% coverage with descriptions for all parameters (e.g., 'Action applied between observations'). The description adds no additional semantic context, meeting the baseline of 3.
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 'Online World Model Training Step' indicates this tool is for training a world model, which distinguishes it from inference tools like wm_predict or wm_encode. However, it does not specify what a 'training step' entails (e.g., weight update, gradient computation), leaving the purpose vague.
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, prerequisites, or alternatives. The description neither states when training steps should be invoked nor contrasts with sibling tools like wm_encode or wm_plan.
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 hints at reading and coupling actions but does not explain side effects, resource consumption, or whether operations are reversible. The description leaves significant uncertainty about 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 concise—a single sentence—but the use of proprietary terms and lack of structure (dash) reduce clarity. It is economical but not optimally informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description fails to mention return values, side effects, or prerequisites. Given the absence of an output schema, the tool description should provide more context about what the tool produces or accomplishes, especially for a complex closed-loop operation.
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 75% (3 of 4 params have descriptions). The tool description adds vague context about the overall closed-loop flow but does not enhance understanding of individual parameters like window_ms or drive_strength beyond what the schema already provides. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description mentions 'closed loop', 'read organoid', 'couple to ASTRA fusion/ROS/ethics', and 'optionally drive the SNN', which gives a general idea of the tool's function but lacks a specific verb and resource. It does not clearly differentiate from sibling tools like inject_spikes or np_send_trigger.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or when not to use it. This forces the agent to rely on trial and error.
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 cover behavioral traits. It only says 'Visual Encoding', which does not disclose side effects, memory usage, rate limits, or what happens to input data (e.g., read vs. destructive). The schema hints at simulation via the 'simulate' parameter, but the description does not clarify behavior during real capture or encoding.
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 (two phrases), which is concise but risks being too terse. It front- loads the technology name ('V-JEPA 2') but lacks any structure or elaboration. While not wasteful, it does not earn its place by providing value beyond a title.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a nested input object with multiple parameters and no output schema, the description is insufficient. It does not explain what the tool returns, how video frames are handled, or the role of 'simulate'. The agent would have to infer behavior from the schema, which is risky.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The schema provides descriptions for the 'input' object (as a whole) and the 'simulate' parameter, and 'videoFrames' is described. The tool description adds no additional meaning beyond what the schema already conveys, so it meets but does not exceed the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'V-JEPA 2 Visual Encoding (Image/Video)' indicates the tool deals with visual encoding for images and videos, distinguishing it from sibling sensors like sensor_audio or sensor_olfactory. However, it lacks a clear verb (e.g., 'encode', 'process') and uses jargon ('V-JEPA 2') that may not be universally understood, leaving the exact action ambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings. The name 'sensor_visual' implies it is for visual data, but the description does not explicitly state use cases, prerequisites, or exclusions. For example, it does not mention that this tool should be used for encoding visual inputs while sensor_audio handles audio.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It only states 'Status & Metrics' without clarifying whether the tool is read-only, if it has side effects, or what specific behavior to expect. This is insufficient for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short (4 words), which is concise, but it sacrifices informativeness. It could include more detail without being verbose, such as listing example metrics. The front-loading is fine, but the content is too sparse.
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 zero parameters, the description should compensate by explaining what the tool returns, but it only says 'Status & Metrics.' This is inadequate for an agent to understand the output format or key data fields, especially given the complexity of a world model.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters (100% coverage), so the description does not need to add parameter details. However, it adds no extra meaning beyond the schema; the phrase 'Status & Metrics' provides minimal context. Given the baseline of 3 for full schema coverage, this score is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'World Model Status & Metrics,' which indicates the tool provides status and metrics for the world model. However, it is vague, lacking specifics on what kind of status or metrics (e.g., training progress, health). Among sibling tools like get_metrics and get_system_status, it distinguishes by focusing on the world model, but still lacks precision.
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 get_metrics or get_system_status. The description does not mention context, prerequisites, or exclusions, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It does not state whether the tool is read-only (though implied by 'query'), nor does it mention any side effects, performance implications, or auth requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the core purpose, but it is too terse to cover necessary details like parameter roles or output format. While concise, it sacrifices completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With three parameters and no output schema, the description is incomplete. It does not describe the return format, data structure, or usage examples, leaving the agent without sufficient context to interpret results.
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 meaning beyond the schema. One parameter (nonzero_only) lacks a description in the schema, and the tool description does not clarify it. Schema coverage is 67%, and the description fails to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it queries 'spikes/minute per electrode over a time window', specifying the metric and resource. However, it does not differentiate from sibling tools like np_count_spikes, which may have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., np_query_spike_events for raw events). The description lacks any context for appropriate use cases 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 exist, so the description carries full burden. It only says 'Reset' without indicating whether it is destructive, requires permissions, or changes persistent state. The behavioral impact is undisclosed.
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?
At two words, it is extremely concise but lacks structure or front-loading of key details. It could be expanded with a brief sentence without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, no annotations, and no parameters, the description is the only source of context. It completely fails to explain what resetting does, side effects, or return values, making it inadequate for a tool likely with significant impact.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are 0 parameters, so the baseline is 4. The description does not need to add parameter information, and the schema describes all (none).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Reset SNN Engine' clearly states the verb (reset) and resource (SNN Engine), but it does not distinguish from similar sibling tools like 'tcai_reset' or explain what specific aspect of the SNN is reset. It is not a tautology but lacks 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?
No guidance is provided on when to use this tool versus alternatives (e.g., 'snn_step', 'simulation_control', 'tcai_reset'). There is no mention of prerequisites, context, or scenarios for resetting.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits like side effects or state requirements. The brief phrase gives no such information, leaving agents unaware of whether the tool is read-only or modifies internal 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 extremely short (two words) and lacks substance. While concise, it fails to provide a functional description; it reads more as a title than a useful explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema or annotations, the description should explain return values and behavior. It does not, leaving agents without critical context for a single-parameter 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 has 100% coverage with descriptions for all properties (targetNeurons, strengths, duration). The tool description adds no additional meaning beyond the schema, but the baseline is 3 due to complete schema 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 'Violation-of-Expectation Detection' clearly indicates the tool's function as detecting when expectations are violated, which is distinct from sibling tools like wm_encode or wm_plan. However, it lacks a verb and doesn't explicitly state what action it performs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., wm_predict or wm_train_step). The description does not mention context, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states 'encoding' but does not disclose whether the operation is read-only, destructive, or has side effects. No mention of permissions, rate limits, or impact on system state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is extremely concise (one line), which is efficient but lacks structure. It front-loads the core purpose but omits any supplementary information. Not every detail is included, so it is not optimally informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with a nested input object and multiple parameters, the description is insufficient. No output schema, no explanation of what the tool returns (latent representation format), and no handling of edge cases (e.g., real audio vs simulation). The schema covers parameters, but the overall usage context is lacking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema coverage is 100% with detailed descriptions for each parameter (e.g., sampleRate, durationMs, simulate). The tool description adds no additional meaning beyond the schema. Baseline 3 is appropriate since schema already documents parameters well.
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?
Description 'A-JEPA Audio Encoding (Waveform → Mel → Latent)' clearly indicates the tool converts audio waveform to latent representation via mel spectrogram. It distinguishes from sibling sensor tools by specifying audio processing. However, it assumes familiarity with A-JEPA jargon, slightly reducing clarity for general AI agents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like sensor_visual or tcai_*. The description does not specify context, prerequisites, or when not to use it. Agent must infer from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as whether the tool performs a read-only operation, accesses hardware, or has side effects. The agent is left guessing about the tool's safety and 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 a single succinct phrase, which is efficient for a tool with no parameters. However, it is too brief to provide meaningful guidance, slightly reducing its usefulness.
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 complex domain with many sibling tools and no output schema or annotations, this minimal description does not adequately inform the agent. It lacks details about the tool's purpose in the pipeline, return format, or behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters with 100% coverage. With no parameters to document, a baseline of 4 is appropriate. The description adds no additional meaning, but it does not need to since there are no parameters.
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 'Multimodal Sensor Pipeline Status' clearly indicates the tool returns status information about a sensor pipeline, but it is vague about what exactly the status contains (e.g., health, data flow, errors). It does not distinguish from sibling tools like sensor_fuse or sensor_process, which may also involve pipelines.
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 sensor_status versus alternative sibling tools. There is no mention of prerequisites, context, or exclusions, leaving the agent without direction on choosing this tool over related ones.
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 convey behavioral traits. It only states the tool processes all modalities and fuses them, but does not disclose side effects, performance implications, or whether it is read-only or destructive. The minimal disclosure leaves significant gaps.
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 that front-loads the core purpose. It avoids verbosity but could be structured to include more details without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 optional parameters, no output schema, no annotations), the description is insufficient. It does not explain return values, expected behavior, or how the fusion works, leaving the agent with incomplete context for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with each parameter described. The description adds context that these parameters are inputs to a multimodal fusion pipeline, which provides some semantic value beyond the schema. However, it does not elaborate on their roles or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Full Multimodal Pipeline (All Modalities → Fused z)' clearly indicates the tool processes multiple sensor modalities and produces a fused output. It distinguishes from sibling tools like sensor_visual, sensor_audio, and sensor_olfactory by implying integration, though the meaning of 'fused z' 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like sensor_fuse or individual modality tools. The description does not specify scenarios where this pipeline is preferable.
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 cover behavioral traits. It mentions 'with inertia' but does not explain what that implies (e.g., statefulness, temporal dependence). There is no disclosure of side effects, required state, or output shape.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, short sentence which is concise, but it may be too brief to convey necessary detail for correct usage. It is not verbose but sacrifices 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?
Given no output schema and no annotations, the description is incomplete. It fails to explain what 'inertia' means, what the output looks like, or any behavioral side effects. For a tool likely involving stateful processing, this is insufficient.
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 add meaning beyond the parameter names. It refers to 'raw signals' but does not map them to the four specific parameters (rewardSignal, novelty, threat, controllability), leaving interpretation to their names alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: appraising raw signals into the PAD emotional space (Mehrabian) with inertia. This is a specific verb-resource combination that distinguishes it from sibling tools like tcai_curiosity or tcai_development.
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 or any prerequisites. It simply describes what it does without contextual usage cues.
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 indicates reading state and optional injection, but does not explain side effects of injection (e.g., whether it permanently alters internal state) or whether the tool is read-only by default. Minimal disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (two sentences) and front-loaded with the tool's purpose. It includes useful interpretation of outputs. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately describes output states. However, it lacks context on how this tool fits into the broader tcai workflow or what happens after injection. Completeness is adequate for a simple state query but could be improved.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already describes the only parameter 'rpe' as injecting a reward-prediction-error sample. The description adds context about output interpretation but doesn't enhance parameter understanding beyond the schema. With 100% schema coverage, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it reads meta-learning state (velocity and novelty spike) and optionally injects RPE. However, it does not explicitly distinguish this tool from siblings like tcai_convergence or tcai_curiosity, which might have overlapping purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description interprets output states ('velocity>0 ⇒ converging; noveltySpike ⇒ novel/confusing regime') but provides no guidance on when to use this tool versus alternatives. For example, it doesn't specify whether to use this before or after tcai_active_inference or tcai_cycle.
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 fully convey behavioral traits. It does not state whether the tool is read-only, has side effects, or if any authentication is needed. It only lists output fields, missing critical behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence but is dense with jargon (e.g., 'qualia', 'sync R') without explanation. It is adequate but could be clearer and more 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?
Given no parameters and no output schema, the description should explain the tool's output comprehensively. It lists items but defines none, leaving key context missing (e.g., format, meaning). Incomplete for an uninitiated 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?
There are no parameters, so schema coverage is 100%. The description adds no parameter info, but that's acceptable since none exist. The baseline for zero parameters 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 states 'Global Neuronal Workspace state' and lists specific aspects (ignition, focus, qualia, etc.), making the tool's purpose clear to those familiar with the domain. It distinguishes from sibling tools like tcai_metrics or tcai_metaconsciousness.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, conditions, or exclusions, leaving the agent without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. 'Predict' implies no side effects, but the required 'action' input suggests conditioning on injections, and it's unclear if the prediction is returned or stored. No mention of mutation or access implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, no wasted words. However, it is too concise to convey necessary information; a bit more detail would improve it without sacrificing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided, yet the description does not mention what the tool returns (e.g., latent state vector). The agent lacks critical information for using the tool effectively, especially given complex sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, with descriptions for both parameters. The description adds no additional semantics beyond the schema. Baseline 3 is appropriate as 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 clearly states the action (predict) and resource (next SNN state in latent space), distinguishing it from siblings like get_snn_state (current state) and wm_encode (encoding). However, it could be more explicit about what 'latent space' means.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., wm_plan, wm_surprise). The description lacks any 'when', 'when not', or references 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 disclose behavioral traits. It mentions 'validate & upload' but does not explain what validation entails, whether it overwrites existing parameters, or any side effects. The description is too brief to convey the tool's full 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 sentence that is front-loaded with the core action. It is concise with no superfluous words, though it could include a bit more context without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 11 parameters, no output schema, and no annotations, the description lacks completeness. It does not mention return values, side effects, prerequisites, or the outcome of upload. The tool's complexity is not fully captured.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is high (82%), leaving little for the description to add. The description provides context ('StimParam', 'charge-balanced biphasic stimulation') but does not elaborate on individual parameters beyond what the schema already includes. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Define, validate & upload') and the resource ('StimParam'), and specifies 'charge-balanced biphasic stimulation', making the purpose unambiguous. It differentiates from sibling tools which are either unrelated or have different verbs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. There is no mention of prerequisites, contexts, or when not to use it. The agent must infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains the mechanism (prediction error between frozen target and online predictor) and default behavior (uses current GNW broadcast). No annotations are provided, so the description carries the full burden. It does not mention side effects, return format, or resource implications, which is a gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact (two sentences) and front-loads the key concept. The first sentence is dense with jargon but efficient. It earns its place without unnecessary information, though could be slightly more streamlined.
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 lacks information about the output (likely a scalar curiosity value), which is critical since no output schema exists. It also does not clarify whether the tool modifies any state or is purely read-only. Given the technical complexity and absence of annotations, these omissions reduce completeness.
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?
With 100% schema description coverage, the schema already documents that 'embedding' is a representation vector defaulting to current broadcast. The tool description adds no additional meaning beyond what the schema provides, so baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as computing an intrinsic-reward/curiosity signal based on prediction error, with a specific algorithm (RNDCuriosity) and purpose (exploration drive proxy for EFE). It differentiates from sibling tools like tcai_active_inference by focusing on novelty detection. However, the verb (e.g., 'computes') is implied rather than explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidance on when to use this tool versus alternatives (e.g., tcai_self_model, tcai_metaconsciousness). It mentions it serves as an epistemic value proxy but lacks when-to-use or when-not-to-use criteria, leaving the agent to infer usage from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose side effects, permission requirements, or whether the tool is read-only. Citing a paper adds context but does not clarify behavioral traits.
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 relatively concise given the number of concepts, though the citation adds minor overhead. It is well-structured, listing components followed by a summary sentence.
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?
Without an output schema, the description should fully explain the return format. It lists components but does not specify data types, units, or structure, leaving gaps for a complex snapshot tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so the description cannot add meaning beyond the schema. With 100% schema coverage and zero params, baseline 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a snapshot of second-order self-evidencing loop state, listing specific components like meta-learning velocity and RND curiosity. It distinguishes from sibling tools by focusing on a composite view, though the verb 'snapshot' is implicit rather than explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus individual siblings like tcai_curiosity or tcai_capability_model. The description only lists contents, leaving the agent to infer use cases.
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 fully disclose behavior. It only states the action (encode) without specifying side effects, data requirements, or whether the operation is read-only or potentially destructive. This is insufficient for an AI 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no extraneous information. It is front-loaded and efficient, earning the highest score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, no output schema, and no annotations, the description is minimal. It does not explain what the latent space is, how encoding works, or what the output represents. For a tool involved in SNN state processing, this leaves significant gaps in understanding.
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 coverage is 100% (empty schema). Per guidelines, 0 parameters warrants a baseline of 4. The description adds purpose (encoding to latent space) beyond the schema, but since there are no parameters, this dimension is inherently high-scoring.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Encode SNN State to Latent Space' clearly identifies a specific verb (encode) and resource (SNN state) with a target (latent space). It is distinct from vague siblings like 'wm_status', but lacks differentiation from 'wm_predict' which may involve similar operations, so it is not a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not indicate when to use this tool versus alternatives like wm_predict or wm_train_step, nor does it mention prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must compensate. It only states the basic action without disclosing side effects, safety, or behavioral constraints like whether repeated triggers have cumulative 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?
Single sentence with no wasted words. However, it could be restructured to front-load the most critical information and improve 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's complexity (2 params, no output schema, no annotations), the description is minimally adequate but lacks details on return values, behavior on errors, or usage 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 coverage is 100% and describes both parameters adequately. The description adds context about the 16-bit trigger array and StimParams, but this does not significantly enhance understanding 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 clearly states the action 'Fire trigger(s)' and the resource 'uploaded StimParams via a 16-bit trigger array'. It is specific and distinguishes from siblings like np_query_triggers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. It implies execution of stimuli but does not mention when not to use or provide context for selection among 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, the description bears full behavioral disclosure burden. It only mentions the retrieval algorithm but does not disclose side effects, read-only nature, or prerequisites like embedding availability.
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 efficiently communicates the tool's purpose and method.
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 5 parameters and no output schema, the description omits details about return values, result format, and interpretation of salience. More context is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 80% (4 of 5 parameters have descriptions). The description adds context about the retrieval method but doesn't elaborate on parameter roles beyond the schema. The embedding parameter's default is noted.
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 action (retrieve memories) and the method (blended cosine similarity, PAD congruence, salience). It distinguishes itself from sibling tools like tcai_memory_store.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description lacks context about when retrieval is appropriate or when to use other memory-related 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 provided, so description carries full burden. It discloses it's a store operation but omits side effects, return value, or effects on existing memories. For a write tool, more behavioral context is needed.
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, front-loaded with action, no unnecessary words. Highly 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?
For a 6-parameter tool with no output schema, the description is too short. It lacks information on return values, idempotency, or how the emotional memory system integrates with other tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 83%, so the schema already documents parameters. The description adds the high-level concept of emotional memory but does not add specific parameter semantics beyond what the schema provides.
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 'store' and resource 'experience in emotional memory', with additional context 'attention-gated, salience-indexed'. It distinguishes from sibling tools like tcai_memory_retrieve and tcai_emotion_appraise.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, such as tcai_memory_retrieve or tcai_emotion_appraise. The parenthetical hint does not constitute usage instructions.
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?
Without annotations, the description carries full burden. It describes the tool as a 'report' implying read-only, but does not explicitly state non-destructive behavior, idempotency, or other traits. This leaves ambiguity for the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, 12 words, front-loaded with key info. No wasted content; every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the description lists what the report contains, it does not specify the return format, units, or whether it's a snapshot. Given no output schema and zero parameters, more detail would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema coverage. Per rubric, baseline is 4. Description adds no extra parameter meaning, which is acceptable.
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 returns a consciousness proxy report with specific metrics (GNW, Effective Information, Φ̃-RIIU, composite score). It differentiates from siblings like 'get_metrics' by specifying these distinct metrics, though it lacks an explicit verb like 'retrieve'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description does not mention when not to use or provide context for selection among 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only mentions returning individual spike timings but fails to disclose whether the operation is read-only, destructive, or requires special permissions. For a query tool this is a notable gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 10 words, efficiently conveying the core purpose. It could be slightly more structured but is not verbose.
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 3 optional parameters and no output schema, the minimal description is partially adequate for a simple query. However, it lacks behavioral context and does not explain the output format, so it is minimally complete.
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 coverage is 33% (only window_sec has a description). The tool description does not add any parameter-level details beyond the schema. With low coverage, the description should compensate, but it provides no additional semantics for the parameters.
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 returns 'individual spike timings over a window', specifying both the resource (spike events) and the action (query). This distinguishes it from sibling tools like np_count_spikes or np_query_spike_count, which focus on counts.
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 usage guidance is provided. The description implies that the tool is for retrieving raw spike timings, which differentiates it from count-based siblings, but it does not state when to use it versus alternatives or any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is the sole source for behavioral disclosure. It explains the conceptual output (stages, stability, score) and mentions 'second-order self-monitoring over time', but does not state whether the tool is read-only or if it affects system state. It lacks explicit safety or side-effect information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with key purpose. However, the first sentence is dense with jargon, slightly reducing clarity. Still efficient overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's tracked concepts but does not specify the return format or data structure. Given no output schema, more detail on what the agent receives would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so baseline score is 4. The description adds value by explaining what the tool computes and returns, providing context beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: longitudinal developmental tracking with coarse stages, stability, and meta-representation score. It uses specific terminology ('DevelopmentTracker port', 'nascent→reactive→integrative→reflective') that distinguishes it from sibling tools like tcai_second_order.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs. alternatives (e.g., when to use tcai_second_order instead). The description does not mention prerequisites, system state dependencies, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It describes the output data but does not indicate whether the tool is read-only, has side effects, requires permissions, or has rate limits. The statement about being 'the principled quantity' suggests importance but not behavioral traits.
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 dense sentence with front-loaded key information and minimal redundancy. The inclusion of academic references (Da Costa et al. 2020; Legros 2026 §4.3) adds credibility but slightly reduces conciseness for an AI agent.
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 complexity of active inference and no output schema, the description lists all returned quantities comprehensively. However, it lacks details on output format, usage context, and behavioral notes, making it adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameters, so schema coverage is effectively 100%. The description adds no parameter information because none exist. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool provides 'Active-inference core telemetry (v2.9)' and enumerates specific quantities (variational free energy F, expected free energy G(π) decomposed, etc.), clearly distinguishing it from sibling tools like tcai_convergence or tcai_metrics by emphasizing it's 'the principled quantity the halting criterion thresholds on — not a heuristic correlate'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for active inference telemetry and halting criterion thresholds, but does not explicitly state when to use or avoid this tool compared to siblings like tcai_convergence or tcai_metrics. No exclusions or alternative recommendations 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 provided, so description carries burden. It explains the tool queries triggers over a window but does not disclose any side effects (e.g., is it read-only? destructive?). The dedup parameter behavior is not explained in description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise single sentence, front-loaded with tool purpose. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and simple parameters, the description is adequate but minimal. It does not describe return format or how dedup affects results, leaving some ambiguity for the 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?
Schema coverage is 100% with descriptions for both parameters. The description adds no additional meaning beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it queries triggers sent to the organoid over a window, with a specific verb and resource. It distinguishes itself from siblings like np_send_trigger (create) and np_query_spike_events (different data type).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. No explicit context for selection among related query tools like np_query_spike_count or np_query_spike_events.
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?
Without annotations, the description must fully disclose behavioral traits. 'Reset' is vague—no mention of destructiveness, data clearing, side effects on other systems, or required permissions. The tool could be destructive, but this is not clarified.
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, no fluff, front-loaded with the action 'Reset' and the resource. Every word serves a purpose.
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 parameterless tool, the description covers the basic action and components. However, it lacks critical behavioral context (e.g., impact on other TCAI subsystems, persistency, or if it's reversible), limiting completeness for an agent deciding to invoke it.
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?
Zero parameters, so schema coverage is 100%. According to guidelines, baseline is 4. Description adds no parameter info needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Reset' and the resource 'TCAI consciousness system', listing specific components (workspace, memory, emotion, metrics). This distinguishes it from sibling tools that perform other operations like checking, getting, or storing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like tcai_metaconsciousness or tcai_cycle. No context about prerequisites, timing, or when not to reset (e.g., during active inference).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must fully disclose behavior. It notes it's a 'proxy, not a measurement', but fails to describe what triggers the evaluation, whether state is modified, or what the return value format is. Lacks critical behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded, and contains no redundant information. Every word adds value, concisely defining the tool's purpose and limitations.
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 having no parameters, the tool lacks an output schema and the description does not explain the return value (e.g., score range, interpretation). Given the simplicity, this omission is significant; an agent cannot properly interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100%. Baseline for 0-parameter tools is 4. The description adds extra meaning by listing the components of the weighted score, which aids understanding even though no parameters 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 clearly states this tool provides a 'weighted score over confidence calibration, learning awareness, self-continuity and error monitoring' via a 'proxy of meta-representation capacity'. It is distinct from sibling tools by focusing specifically on a meta-consciousness composite.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for evaluating meta-consciousness but does not explicitly state when to use this tool vs alternatives (e.g., tcai_second_order, tcai_self_model). No exclusions or context are provided.
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 full burden for behavioral disclosure. It mentions 'query' and 'list', implying read-only behavior, but does not explicitly state side effects, safety, permissions, or error handling (e.g., what happens if action is not found). This lack of detail 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: two sentences with no wasted words. It front-loads the key information (model type and purpose) followed by usage. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple query tool with one optional parameter, the description covers the main functionality. However, it omits details about the return format or data structure (e.g., what 'expected-valence map' looks like). Since no output schema exists, the description could be more complete by describing the output, but it remains mostly sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers the single 'action' parameter with a description. The tool description adds value by explaining the underlying 'action → expected-valence map' and the default behavior (listing if no action). This provides semantic context beyond the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Query expected outcome of an action, or list the learned capability table.' It identifies the resource (capability model) and the action (query/list), distinguishing it from sibling tcai_* tools that handle different aspects like self-model or metacognition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for querying expected valences or listing the table, but provides no explicit guidance on when to use this tool versus alternatives (e.g., tcai_self_model). No exclusions or alternative recommendations are given, leaving the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description outlines the cycle stages and early stopping behavior, but lacks disclosure about side effects, state mutations, or required permissions, which is important given no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence listing the pipeline, followed by a concise explanation of early stopping. No 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 complexity (17 params, no output schema, no annotations), the description provides core functionality but lacks details on return values, error conditions, and complete parameter semantics.
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?
With 82% schema coverage, the description adds marginal value by referencing stopWhenSatisfied and some thresholds, but does not explain all 17 parameters in depth.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Run one or more ACM cycles' and lists the pipeline stages, distinguishing it from sibling tools that focus on specific aspects like active inference or convergence.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for running multiple cycles with early stopping via stopWhenSatisfied, but does not explicitly contrast with other tcai tools or specify when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It describes counting spikes per electrode over a window, suggesting a read operation, but does not disclose return format, permissions, or side effects. Adequate but not detailed.
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, zero waste, front-loaded with key information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters, no output schema, and no annotations, the description is fairly complete. It explains the tool's function and parameter relevance. Could mention output format but not required.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions. The description adds context about 'spikes per electrode' and 'N-ms window', reinforcing the parameter meanings. Slight value add beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool counts spikes per electrode over a time window, with specific verb 'count' and resource 'spikes per electrode'. It distinguishes from siblings like 'np_query_spike_count' by mentioning 'Closed-loop' and 'per electrode'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage in a closed-loop context but provides no explicit guidance on when to use this tool versus alternatives like 'np_query_spike_events' or 'np_query_spike_count'. No exclusions or prerequisites are stated.
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?
Describes the procedural steps (runs cycles, records ΔF, sets threshold) and states return values. However, with no annotations, it lacks disclosure of side effects (e.g., whether it modifies persistent state), error behavior, or permission requirements. The description is functional but not fully comprehensive.
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, well-structured paragraph with no redundancy. Every sentence contributes essential information. The purpose is stated first, followed by method and outputs. No extraneous detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of output schema and annotations, the description covers input parameters, process, and return values. It does not mention error conditions or prerequisites, but for a calibration tool the provided information is largely sufficient for a knowledgeable user.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for each parameter. The tool description adds meaning by explaining how parameters are used (warm-up cycles, reward signal during warm-up, factor applied to median) and provides defaults. This enriches the schema information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly identifies the tool's action (calibrate halting threshold), the method (using measured ΔF scale instead of guessed constant), and the specific outcome (returns measured ΔF scale and applied threshold). Names specific parameters (cycles, reward, factor) and explicitly addresses a known critique, distinguishing it from generic alternatives.
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?
Implies usage context by referencing a 'v2.9 critique', but does not explicitly state when to use this tool versus sibling tools (e.g., tcai_cycle, tcai_convergence). No guidance on prerequisites, when not to use, or recommended contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It thoroughly explains the halting criterion: three conditions (free energy, task quality, epistemic value) and patience. It also mentions the nuance that stationarity alone is insufficient, citing a reference. This is highly transparent.
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, the first concise and front-loaded. The second sentence is longer but packs essential logic. The citation might be extraneous for an agent, but overall it's efficient and clear.
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?
No output schema exists, but the description does not detail the return value structure (e.g., fields of satisfaction state, thresholds). It mentions 'current satisfaction state and active thresholds' but is vague. Given the complexity, more specifics would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with individual parameter descriptions. The description adds value by explaining how parameters combine to form the halting condition, which is not obvious from the schema alone. This contextualizes the parameters beyond their syntax.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool inspects or configures the recursive double-loop halting criterion. It specifies the dual behavior with and without arguments, making the purpose unmistakable. Among numerous tcai_ tools, this one is uniquely about convergence halting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains that with no arguments it returns state, with arguments it updates. It provides the halting condition in detail, guiding when the tool is useful. However, it does not explicitly contrast with sibling tools or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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