Skip to main content
Glama

compose

Merge two system graphs into a unified model with entity linking to analyze combined topology and dependencies.

Instructions

Merge two EN graphs into one with entity linking.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
source_aYesEN source code for the first system
source_bYesEN source code for the second system
linksYesEntity links between the two systems (e.g. 'a.node1=b.node2, a.node3=b.node4')

Implementation Reference

  • Registration and implementation of the 'compose' tool, which merges two EN graphs into one using entity links by calling the configured EN API.
    server.tool(
      "compose",
      "Merge two EN graphs into one with entity linking.",
      {
        source_a: z.string().describe("EN source code for the first system"),
        source_b: z.string().describe("EN source code for the second system"),
        links: z
          .string()
          .describe(
            "Entity links between the two systems (e.g. 'a.node1=b.node2, a.node3=b.node4')"
          ),
      },
      async ({ source_a, source_b, links }) => {
        const result = await callApi("compose", { source_a, source_b, links });
        return {
          content: [{ type: "text" as const, text: result.text }],
          isError: result.isError,
        };
      }
    );
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool merges graphs with entity linking, implying a write or transformation operation, but doesn't detail what 'merge' entails—e.g., whether it creates a new graph, modifies inputs, or handles conflicts. It also omits information on permissions, rate limits, or error conditions, which are critical for a tool that likely performs complex graph operations.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action ('Merge two EN graphs') and includes the key detail ('with entity linking'), making it easy to parse and understand quickly.

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 the complexity of merging graphs with entity linking, the description is insufficient. With no annotations and no output schema, it fails to explain what the merged result looks like, how entity linking affects the output, or any behavioral traits like error handling. For a tool with three required parameters and no structured safety or output information, more descriptive context is needed to guide effective use.

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%, with each parameter clearly documented in the input schema (e.g., 'source_a' as 'EN source code for the first system'). The description adds minimal value beyond this, only implying that parameters relate to merging and linking without providing additional syntax or format details. This meets the baseline for high schema coverage.

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 action ('Merge') and the resources ('two EN graphs'), specifying that entity linking is involved. It distinguishes itself from siblings like 'diff' or 'analyze_system' by focusing on combination rather than comparison or analysis. However, it doesn't explicitly differentiate from potential similar tools like 'evolve' or 'trace', which might also involve graph operations.

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 prerequisites, such as needing valid EN source code, or specify scenarios where merging is appropriate compared to using other tools like 'diff' for comparison or 'analyze_system' for inspection. This lack of context leaves the agent without clear usage instructions.

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/dushyant30suthar/endiagram-mcp'

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