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twining_read

Read and filter blackboard entries from the Twining MCP Server to check agent posts, find relevant context, or review recent activity with optional type, tag, scope, and timestamp filters.

Instructions

Read blackboard entries with optional filters. Use this to check what other agents have posted, find relevant context, or review recent activity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entry_typesNoFilter by entry type(s)
tagsNoFilter by tags (OR match)
scopeNoFilter by scope (prefix match)
sinceNoOnly entries after this ISO 8601 timestamp
limitNoMax entries to return (default: 50)

Implementation Reference

  • The `read` method in `BlackboardEngine` handles the logic for reading blackboard entries, which is called by the `twining_read` MCP tool.
    /** Read blackboard entries with optional filters. */
    async read(filters?: {
      entry_types?: string[];
      tags?: string[];
      scope?: string;
      since?: string;
      limit?: number;
    }): Promise<{ entries: BlackboardEntry[]; total_count: number }> {
      return this.store.read({
        entry_types: filters?.entry_types,
        tags: filters?.tags,
        scope: filters?.scope,
        since: filters?.since,
        limit: filters?.limit ?? 50,
      });
    }
  • The `twining_read` tool registration in `src/tools/blackboard-tools.ts`. It defines the input schema and calls `engine.read(args)`.
    // twining_read — Read blackboard entries with optional filters
    server.registerTool(
      "twining_read",
      {
        description:
          "Read blackboard entries with optional filters. Use this to check what other agents have posted, find relevant context, or review recent activity.",
        inputSchema: {
          entry_types: z
            .array(z.string())
            .optional()
            .describe("Filter by entry type(s)"),
          tags: z
            .array(z.string())
            .optional()
            .describe("Filter by tags (OR match)"),
          scope: z
            .string()
            .optional()
            .describe("Filter by scope (prefix match)"),
          since: z
            .string()
            .refine((val) => !isNaN(Date.parse(val)), {
              message: "Must be a valid ISO 8601 timestamp",
            })
            .optional()
            .describe("Only entries after this ISO 8601 timestamp"),
          limit: z
            .number()
            .optional()
            .describe("Max entries to return (default: 50)"),
        },
      },
      async (args) => {
        try {
          const result = await engine.read(args);
          return toolResult(result);
        } catch (e) {
          return toolError(
            e instanceof Error ? e.message : "Unknown error",
            "INTERNAL_ERROR",
          );
        }
      },
    );
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 the tool is for reading (implied safe operation) and describes some use cases, but doesn't disclose important behavioral traits like whether it requires authentication, rate limits, pagination behavior, error conditions, or what format the entries are returned in. The description is insufficient for a tool with 5 parameters and no output schema.

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 perfectly concise with two sentences that each earn their place. The first sentence states the core functionality, the second provides usage scenarios. No wasted words, well-structured, and front-loaded with the essential 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?

For a read tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'blackboard entries' are in this context, what format they're returned in, whether there's pagination beyond the 'limit' parameter, or any error conditions. The usage scenarios help but don't compensate for missing behavioral and output information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value beyond the schema - it mentions 'optional filters' which is already clear from the parameter names and descriptions. No additional semantic context is provided about how filters combine or what 'blackboard entries' actually contain.

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: 'Read blackboard entries with optional filters.' It specifies the verb ('Read') and resource ('blackboard entries'), and distinguishes it from siblings by focusing on reading rather than writing or modifying. However, it doesn't explicitly differentiate from similar read operations like 'twining_recent' or 'twining_query'.

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

Usage Guidelines4/5

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

The description provides clear usage context: 'Use this to check what other agents have posted, find relevant context, or review recent activity.' This gives practical scenarios for when to use the tool. However, it doesn't explicitly mention when NOT to use it or name alternative tools for similar purposes.

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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