Inspect the self-representation state of a bio-hybrid neuromorphic simulation, covering interoception, epistemic model, temporal continuity, and attention schema for consciousness proxy assessment.
Self-representation state: interoception, epistemic model, temporal continuity, attention schema
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Changes observed during successful MCP inspections.
v3.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavioral traits. It reveals the content areas of the state but says nothing about side effects, access requirements, or whether the state is computed on demand or cached. It does not explicitly state that this is a read-only operation, which is a significant gap for a tool that likely returns a snapshot.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact phrase that front-loads the core concept ('Self-representation state') and then lists four concrete components. There is no redundancy or filler. Every word contributes to the meaning, making it appropriately concise and well-structured for a zero-parameter state query.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters, no output schema, and no annotations, the description gives enough to infer the tool's domain but not the full picture. It fails to mention the return type or how the state is presented (e.g., JSON object, numerical values). It is minimally viable but leaves notable gaps, especially around interpreting the listed components.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description correctly does not need to elaborate on parameter meaning. The empty input schema is consistent with the description, and no further clarification is required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the tool as exposing 'Self-representation state' with specific components (interoception, epistemic model, etc.), which gives a clear subject area. However, it lacks an explicit verb (e.g., 'get', 'retrieve', 'display'), so the exact action is implied rather than stated. It partially distinguishes from siblings by listing unique aspects, but without a verb it remains somewhat vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like tcai_second_order or tcai_workspace_state. It does not mention any exclusions, prerequisites, or typical use cases. The reader is left to infer that this is a state query, but no explicit direction is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.