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blackboard_dump

Retrieve the complete, non-expired state of the collaborative blackboard to view all current beliefs and research data shared by agents.

Instructions

Return the full (non-expired) blackboard state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The tool handler function for 'blackboard_dump'. Uses the @mcp.tool() decorator to register with FastMCP. Delegates to Blackboard.dump() to return the full non-expired state.
    @mcp.tool()
    def blackboard_dump() -> dict[str, Any]:
        """Return the full (non-expired) blackboard state."""
        return _BLACKBOARD.dump()
  • The Blackboard.dump() helper method. Iterates the in-memory store, filters out expired beliefs, and returns each as a dict (via Belief.to_dict()).
    def dump(self) -> dict[str, dict[str, Any]]:
        with self._lock:
            return {
                k: b.to_dict()
                for k, b in self._store.items()
                if not b.expired()
            }
  • The @mcp.tool() decorator registers the function as an MCP tool named 'blackboard_dump' with the FastMCP server.
    @mcp.tool()
    def blackboard_dump() -> dict[str, Any]:
  • The function signature acts as the schema: no input parameters, returns a dict[str, Any].
    def blackboard_dump() -> dict[str, Any]:
        """Return the full (non-expired) blackboard state."""
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description mentions 'non-expired', indicating that expired entries are excluded, which is a behavioral detail. However, with no annotations, it lacks disclosure of side effects, performance implications, or whether it's a snapshot. More context would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with 6 words, very concise and directly to the point. 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?

The description is minimal but covers the essential purpose. However, the tool has an output schema, and the description does not hint at the return format (e.g., JSON object). For a dump tool, specifying the output structure would enhance 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 input schema has no parameters, so the description does not need to add parameter meaning. Schema coverage is 100% trivially. The description is adequate.

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 the full non-expired blackboard state. The verb 'Return' and resource 'full (non-expired) blackboard state' are specific. It distinguishes from siblings like blackboard_get (single key) and blackboard_query (filtered), and from math tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like blackboard_get or blackboard_query. The description only states what it does, without explicitly advising when to use it or when not to.

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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