ER diagrams from SQL, Prisma or ORM models. One tool, no account, nothing stored.
Scored across 1 tool
Only one tool exists, so there is nothing to confuse it with. Its purpose—turning schema text into a hosted interactive ER diagram link—is stated explicitly and narrowly.
The single name open_er_diagram follows a clear verb_noun snake_case convention. No conflicting conventions are present to create inconsistency.
The server's scope is genuinely singular (generate an ER diagram link), so one tool is defensible, but the surface is thin with no companion operations. It sits at the borderline of the 1-2 tool range.
For the narrow task of producing an ER diagram, the tool covers many input formats and explains fallbacks for oversized schemas. Minor gaps remain—no direct database connection or diagram export—but agents can work around these via the generated link.
Open an interactive ER diagram of a database schema on erddocs.com. Use it when the user wants to see, understand, review or document their database structure, or asks for an ER diagram.
Pass the schema text exactly as it is in the project. It reads SQL dumps and DDL (PostgreSQL, MySQL, SQL Server, SQLite), Prisma schema.prisma, DBML, Mermaid erDiagram, PlantUML, Django models.py, SQLAlchemy models, Sequelize models, TypeORM entities and a folder of Laravel migrations (concatenate the files). For SQL, the structure is enough: no INSERT data.
Returns a link. The user opens it in a browser and gets the diagram with every table and relation laid out, relations without declared foreign keys found by column name and drawn dashed, and a data dictionary. Nothing is stored on the server: the schema travels inside the link and is parsed in the user's browser. A link holds a schema of a few dozen tables (about 100–200 characters of link per table, depending on comments); for a larger one the tool says so and the user pastes it on erddocs.com instead.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Only if automatic detection would get it wrong. | |
| schema | Yes | The schema text as it is: SQL DDL, schema.prisma, models.py, migrations, DBML, Mermaid or PlantUML. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only declare it as a read-only, idempotent, closed-world operation; the description adds substantial context beyond that: it returns a link, the schema is embedded in the link rather than stored server-side, parsing happens in the user's browser, relations without declared FKs are inferred by column name and drawn dashed, and a data dictionary is included.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three paragraphs with the purpose front-loaded, then input expectations, then return behavior and limits — a logical order. It is on the long side and the link-size arithmetic is somewhat detailed, but each sentence carries information an agent would otherwise have to guess.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With only two parameters and no output schema, the description fully covers what the agent needs: accepted input formats, what to omit, what is returned, privacy characteristics, and the size limit with its workaround. Nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is already 100%, yet the description still adds real meaning: pass the text exactly as-is, SQL needs only structure (no INSERT data), and Laravel migration folders must be concatenated. It also enumerates the supported dialects that map onto the format enum, clarifying when auto-detection suffices.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource — 'Open an interactive ER diagram of a database schema' — plus the destination (erddocs.com). There are no siblings to disambiguate from, and the description makes the tool's output immediately identifiable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives explicit triggering conditions ('when the user wants to see, understand, review or document their database structure, or asks for an ER diagram'). It also states the fallback path for oversized schemas (paste directly on erddocs.com), so the agent knows what happens at the boundary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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