Skip to main content
Glama

bear_context_setup

Idempotent

Set up a curated, synced folder of Bear notes for LLM consumption. This one-time operation creates the directory structure and config; tag notes with #context to pull them in later.

Instructions

Initialize a context library — a curated, synced folder of Bear notes optimized for LLM consumption. Creates the directory structure and config. After setup, tag Bear notes with #context (or a custom prefix) and use bear_context_sync to pull them in. One-time operation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dirNoOutput directory for the context library (default: ~/.bear-context)
tag_prefixNoTag prefix for qualifying notes (default: context). Notes tagged #context or #context/subtag will be included.
use_frontmatterNoAlso include notes with context: true in YAML front matter (default: true)

Implementation Reference

  • Input schema for the bear_context_setup tool, defining three optional parameters: dir (directory for context library), tag_prefix (tag prefix for qualifying notes), and use_frontmatter (include notes with context:true front matter).
    inputSchema: {
      type: "object" as const,
      properties: {
        dir: {
          type: "string",
          description:
            "Output directory for the context library (default: ~/.bear-context)",
        },
        tag_prefix: {
          type: "string",
          description:
            "Tag prefix for qualifying notes (default: context). Notes tagged #context or #context/subtag will be included.",
        },
        use_frontmatter: {
          type: "boolean",
          description:
            "Also include notes with context: true in YAML front matter (default: true)",
        },
      },
    },
    annotations: {
      readOnlyHint: false,
      destructiveHint: false,
      idempotentHint: true,
    },
  • Registration of the bear_context_setup tool as an entry in the tools record. Includes the tool definition (name, description, inputSchema, annotations) and the buildArgs function that translates input parameters into CLI arguments for the bcli backend ('context init --json').
    bear_context_setup: {
      tool: {
        name: "bear_context_setup",
        description:
          "Initialize a context library — a curated, synced folder of Bear notes optimized for LLM consumption. Creates the directory structure and config. After setup, tag Bear notes with #context (or a custom prefix) and use bear_context_sync to pull them in. One-time operation.",
        inputSchema: {
          type: "object" as const,
          properties: {
            dir: {
              type: "string",
              description:
                "Output directory for the context library (default: ~/.bear-context)",
            },
            tag_prefix: {
              type: "string",
              description:
                "Tag prefix for qualifying notes (default: context). Notes tagged #context or #context/subtag will be included.",
            },
            use_frontmatter: {
              type: "boolean",
              description:
                "Also include notes with context: true in YAML front matter (default: true)",
            },
          },
        },
        annotations: {
          readOnlyHint: false,
          destructiveHint: false,
          idempotentHint: true,
        },
      },
      buildArgs: (input) => {
        const args = ["context", "init", "--json"];
        if (input.dir) args.push("--dir", String(input.dir));
        if (input.tag_prefix)
          args.push("--tag-prefix", String(input.tag_prefix));
        if (input.use_frontmatter === true) args.push("--frontmatter");
        return args;
      },
    },
  • The buildArgs function that constructs CLI arguments for the bear_context_setup tool. It builds arguments ['context', 'init', '--json'] and conditionally appends --dir, --tag-prefix, and --frontmatter flags based on input parameters. This is the core logic that drives execution when the tool is called.
      buildArgs: (input) => {
        const args = ["context", "init", "--json"];
        if (input.dir) args.push("--dir", String(input.dir));
        if (input.tag_prefix)
          args.push("--tag-prefix", String(input.tag_prefix));
        if (input.use_frontmatter === true) args.push("--frontmatter");
        return args;
      },
    },
  • The bear_context_setup tool is registered via the ListToolsRequestSchema handler in index.ts, which exposes all tools from the tools.ts registry to the MCP client.
    server.setRequestHandler(ListToolsRequestSchema, async () => ({
      tools: Object.values(tools).map((t) => t.tool),
    }));
  • The CallToolRequestSchema handler dispatches tool calls. When bear_context_setup is called, it invokes the tool's buildArgs to construct CLI arguments, then executes them via execBcliWithReauth (which calls the external 'bcli' binary with 'context init --json' and optional flags).
    server.setRequestHandler(CallToolRequestSchema, async (request) => {
      const { name, arguments: input } = request.params;
      const handler = tools[name];
    
      if (!handler) {
        return {
          content: [{ type: "text", text: `Unknown tool: ${name}` }],
          isError: true,
        };
      }
    
      const params = (input ?? {}) as Record<string, unknown>;
    
      // Validate bear_edit_note: need at least one edit operation
      if (name === "bear_edit_note") {
        const hasAppend = params.append_text !== undefined;
        const hasBody = params.body !== undefined;
        const hasSetFm = params.set_frontmatter !== undefined &&
          Object.keys(params.set_frontmatter as object).length > 0;
        const hasRemoveFm = Array.isArray(params.remove_frontmatter) &&
          (params.remove_frontmatter as unknown[]).length > 0;
        const hasFm = hasSetFm || hasRemoveFm;
    
        if (!hasAppend && !hasBody && !hasFm) {
          return {
            content: [
              {
                type: "text",
                text: "Provide 'append_text', 'body', 'set_frontmatter', or 'remove_frontmatter'.",
              },
            ],
            isError: true,
          };
        }
        if (hasAppend && hasBody) {
          return {
            content: [
              {
                type: "text",
                text: "Provide either 'append_text' or 'body', not both.",
              },
            ],
            isError: true,
          };
        }
      }
    
      try {
        const args = handler.buildArgs(params);
        let result: { stdout: string; stderr: string };
    
        // Check if this tool needs stdin piping
        const stdinData = handler.usesStdin?.(params) ?? null;
        if (stdinData !== null) {
          result = await execBcliWithStdinAndReauth(args, stdinData);
        } else {
          result = await execBcliWithReauth(args);
        }
    
        // Parse JSON output from bcli
        const stdout = result.stdout.trim();
        if (!stdout) {
          return {
            content: [{ type: "text", text: "Command completed successfully." }],
          };
        }
    
        // Validate it's JSON and pretty-print
        try {
          const parsed = JSON.parse(stdout);
          return {
            content: [
              { type: "text", text: JSON.stringify(parsed, null, 2) },
            ],
          };
        } catch {
          // If bcli returned non-JSON, pass it through
          return {
            content: [{ type: "text", text: stdout }],
          };
        }
      } catch (error) {
        const message =
          error instanceof BcliError ? error.message : String(error);
        return {
          content: [{ type: "text", text: message }],
          isError: true,
        };
      }
    });
Behavior5/5

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

Description adds that it creates directory structure and config, and states 'one-time operation' which aligns with idempotentHint, going beyond 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?

Two concise sentences, front-loaded with the core action, no redundant information.

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

Completeness5/5

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

Fully explains setup as a one-time operation with no output needed, complete for a simple initialization 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?

Schema coverage is 100% and description adds context about the 'custom prefix', but use_frontmatter is not elaborated; still adds value over 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?

Description clearly identifies the tool as initializing a context library, a specific verb-resource pair, and distinguishes it from siblings like bear_context_sync.

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

Usage Guidelines5/5

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

Explicitly states it is a one-time operation and directs the user to use bear_context_sync after tagging notes, providing clear usage context.

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/KuvopLLC/better-bear'

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