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Discourse MCP

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by discourse

Read Topic

discourse_read_topic

Retrieve topic metadata and posts from Discourse forums to analyze discussions, extract information, or monitor conversations.

Instructions

Read a topic metadata and first N posts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topic_idYes
post_limitNo
start_post_numberNoStart from this post number (1-based)

Implementation Reference

  • The main handler function that fetches Discourse topic metadata and up to N posts using API calls, handles pagination and limits, formats the output as structured text with title, posts, and link.
    async ({ topic_id, post_limit = 5, start_post_number }, _extra: any) => {
      try {
        const { base, client } = ctx.siteState.ensureSelectedSite();
        const start = start_post_number ?? 1;
    
        // First request to load metadata/title and initial chunk
        let current = start;
        let fetchedPosts: Array<{ number: number; username: string; created_at: string; content: string }> = [];
        let slug = "";
        let title = `Topic ${topic_id}`;
        let category = "";
        let tags: string[] = [];
    
        const maxBatches = 10; // safety guard
        const limit = Number.isFinite(ctx.maxReadLength) ? ctx.maxReadLength : 50000;
        for (let i = 0; i < maxBatches && fetchedPosts.length < post_limit; i++) {
          // Ask for raw content when possible
          const url = current > 1 ? `/t/${topic_id}.json?post_number=${current}&include_raw=true` : `/t/${topic_id}.json?include_raw=true`;
          const data = (await client.get(url)) as any;
          if (i === 0) {
            title = data?.title || title;
            category = data?.category_id ? `Category ID ${data.category_id}` : "";
            tags = Array.isArray(data?.tags) ? data.tags : [];
            slug = data?.slug || String(topic_id);
          }
          const stream: any[] = Array.isArray(data?.post_stream?.posts) ? data.post_stream.posts : [];
          const sorted = stream.slice().sort((a, b) => (a.post_number || 0) - (b.post_number || 0));
          const filtered = sorted.filter((p) => (p.post_number || 0) >= current);
          for (const p of filtered) {
            if (fetchedPosts.length >= post_limit) break;
            fetchedPosts.push({
              number: p.post_number,
              username: p.username,
              created_at: p.created_at,
              content: (p.raw || p.cooked || p.excerpt || "").toString().slice(0, limit),
            });
          }
          if (filtered.length === 0) break; // no progress
          current = (filtered[filtered.length - 1]?.post_number || current) + 1;
        }
    
        const lines: string[] = [];
        lines.push(`# ${title}`);
        if (category) lines.push(category);
        if (tags.length) lines.push(`Tags: ${tags.join(", ")}`);
        lines.push("");
        for (const p of fetchedPosts) {
          lines.push(`- Post #${p.number} by @${p.username} (${p.created_at})`);
          lines.push(`  ${p.content}`);
        }
        lines.push("");
        lines.push(`Link: ${base}/t/${slug}/${topic_id}`);
        return { content: [{ type: "text", text: lines.join("\n") }] };
      } catch (e: any) {
        return { content: [{ type: "text", text: `Failed to read topic ${topic_id}: ${e?.message || String(e)}` }], isError: true };
      }
    }
  • Zod input schema validating topic_id (required positive integer), optional post_limit (1-100), optional start_post_number (1-based).
    const schema = z.object({
      topic_id: z.number().int().positive(),
      post_limit: z.number().int().min(1).max(100).optional(),
      start_post_number: z.number().int().min(1).optional().describe("Start from this post number (1-based)")
    });
  • Registers the 'discourse_read_topic' tool with MCP server, providing title, description, input schema, and handler function.
    server.registerTool(
      "discourse_read_topic",
      {
        title: "Read Topic",
        description: "Read a topic metadata and first N posts.",
        inputSchema: schema.shape,
      },
      async ({ topic_id, post_limit = 5, start_post_number }, _extra: any) => {
        try {
          const { base, client } = ctx.siteState.ensureSelectedSite();
          const start = start_post_number ?? 1;
    
          // First request to load metadata/title and initial chunk
          let current = start;
          let fetchedPosts: Array<{ number: number; username: string; created_at: string; content: string }> = [];
          let slug = "";
          let title = `Topic ${topic_id}`;
          let category = "";
          let tags: string[] = [];
    
          const maxBatches = 10; // safety guard
          const limit = Number.isFinite(ctx.maxReadLength) ? ctx.maxReadLength : 50000;
          for (let i = 0; i < maxBatches && fetchedPosts.length < post_limit; i++) {
            // Ask for raw content when possible
            const url = current > 1 ? `/t/${topic_id}.json?post_number=${current}&include_raw=true` : `/t/${topic_id}.json?include_raw=true`;
            const data = (await client.get(url)) as any;
            if (i === 0) {
              title = data?.title || title;
              category = data?.category_id ? `Category ID ${data.category_id}` : "";
              tags = Array.isArray(data?.tags) ? data.tags : [];
              slug = data?.slug || String(topic_id);
            }
            const stream: any[] = Array.isArray(data?.post_stream?.posts) ? data.post_stream.posts : [];
            const sorted = stream.slice().sort((a, b) => (a.post_number || 0) - (b.post_number || 0));
            const filtered = sorted.filter((p) => (p.post_number || 0) >= current);
            for (const p of filtered) {
              if (fetchedPosts.length >= post_limit) break;
              fetchedPosts.push({
                number: p.post_number,
                username: p.username,
                created_at: p.created_at,
                content: (p.raw || p.cooked || p.excerpt || "").toString().slice(0, limit),
              });
            }
            if (filtered.length === 0) break; // no progress
            current = (filtered[filtered.length - 1]?.post_number || current) + 1;
          }
    
          const lines: string[] = [];
          lines.push(`# ${title}`);
          if (category) lines.push(category);
          if (tags.length) lines.push(`Tags: ${tags.join(", ")}`);
          lines.push("");
          for (const p of fetchedPosts) {
            lines.push(`- Post #${p.number} by @${p.username} (${p.created_at})`);
            lines.push(`  ${p.content}`);
          }
          lines.push("");
          lines.push(`Link: ${base}/t/${slug}/${topic_id}`);
          return { content: [{ type: "text", text: lines.join("\n") }] };
        } catch (e: any) {
          return { content: [{ type: "text", text: `Failed to read topic ${topic_id}: ${e?.message || String(e)}` }], isError: true };
        }
      }
    );
  • Invokes the registerReadTopic function as part of registering all builtin tools in the MCP server.
    registerReadTopic(server, ctx, { allowWrites: false });
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 reading metadata and posts, implying a read-only operation, but doesn't specify authentication requirements, rate limits, error conditions, or what 'first N posts' means in practice (e.g., ordering, pagination). The description is minimal and lacks critical operational context.

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 extremely concise—a single sentence that directly states the tool's function without any fluff. It's front-loaded with the core action and resource, making it efficient and easy to parse. Every word earns its place.

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?

Given 3 parameters with low schema coverage (33%), no annotations, and no output schema, the description is insufficient. It doesn't explain return values, error handling, or important behavioral aspects like what 'metadata' includes or how posts are ordered. For a tool with this complexity and lack of structured data, more descriptive context is needed.

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 33% (only start_post_number has a description). The description mentions 'first N posts' which hints at post_limit, but doesn't explain topic_id or provide additional context beyond the schema. Since schema coverage is low (<50%), the description should compensate more but only adds marginal value, warranting a baseline 3.

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 verb 'Read' and the resource 'topic metadata and first N posts', making the purpose unambiguous. It distinguishes from siblings like discourse_read_post (which reads individual posts) and discourse_filter_topics (which filters topics rather than reading a specific one). However, it doesn't explicitly contrast with all siblings, keeping it at 4 rather than 5.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over discourse_read_post for reading posts within a topic, or when to use discourse_filter_topics for topic discovery. There's no context about prerequisites, permissions, or typical use cases.

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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