Skip to main content
Glama

blackboard_get

Retrieve a belief by key from the shared blackboard. Returns None if the key is missing or expired.

Instructions

Read a single belief by key. Returns None if absent or expired.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool handler for blackboard_get — reads a belief by key from the blackboard and returns it as a dict, or None if absent/expired.
    def blackboard_get(key: str) -> dict[str, Any] | None:
        """Read a single belief by key. Returns None if absent or expired."""
        b = _BLACKBOARD.read(key)
        return None if b is None else b.to_dict()
  • The Belief dataclass that defines the schema for blackboard entries, including key, value, source, confidence, ttl, tags, and provenance.
    @dataclass
    class Belief:
        key: str
        value: Any
        source: str
        confidence: float = 0.7
        ts: float = field(default_factory=time.time)
        ttl: Optional[float] = None
        tags: list[str] = field(default_factory=list)
        provenance: dict[str, Any] = field(default_factory=dict)
    
        def expired(self) -> bool:
            return self.ttl is not None and (time.time() - self.ts) > self.ttl
    
        def to_dict(self) -> dict[str, Any]:
            return self.__dict__.copy()
  • The Blackboard.read() helper method called by blackboard_get — retrieves a belief by key, returns None if missing or expired.
    def read(self, key: str) -> Optional[Belief]:
        with self._lock:
            b = self._store.get(key)
            if b is None:
                return None
            if b.expired():
                del self._store[key]
                return None
            return b
  • The FastMCP server instance that registers the tool via the @mcp.tool() decorator on line 56.
    mcp = FastMCP("mcp-research-collective")
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses two failure modes (absent or expired) and the return value. For a simple read tool, this is adequate.

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 efficiently conveys purpose, behavior, and return value. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no nested objects, output schema exists), the description covers the key aspects: operation, parameter role, and return value. Minor gaps on key semantics are not critical.

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 does not add any detail beyond the schema regarding the `key` parameter format, constraints, or examples. With 0% schema description coverage, the description fails to compensate.

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 reads a single belief by key, which is a specific verb and resource. It distinguishes from siblings like `blackboard_query` (likely multiple beliefs) and `blackboard_set` (write).

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 reading a single belief but provides no explicit guidance on when to use this tool versus alternatives like `blackboard_query`. It mentions return conditions but not context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/SiegKat/mcp-agent-blackboard'

If you have feedback or need assistance with the MCP directory API, please join our Discord server