OraViz MCP

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does disclose that the query is read-only and that the output is an image plus a data preview, which is helpful. It stops short of describing limitations, error behavior, or how the image is represented.

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 compact sentences deliver high-value information without filler. The read-only and chart-image purpose is front-loaded, followed by chart types and column mapping, then the return payload.

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?

The description covers the core invocation details and return payload, but no output schema exists and the description does not specify how the PNG image is delivered (URL, base64, file path). It also leaves the label parameters undocumented, so the definition is workable but not fully complete.

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 0%, so the description must compensate. It adds important meaning: the list of valid chart_type values, how columns map to axes and series, and the read-only nature of the sql parameter. The optional title, x_label, and y_label parameters are left to inference, but their names are largely self-explanatory.

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?

The description states a specific action: run a read-only SQL query and render the result as a PNG chart. It clearly distinguishes itself from sibling tools like execute_query by emphasizing chart generation, not just raw query results.

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?

The intended use is implied: use this tool when you need a visual chart from SQL results. However, it does not explicitly say when to prefer this over execute_query or other sibling tools, nor does it mention any exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden. It explicitly states read-only behavior, preview-capping, and summarisation of large cells, which are key behavioral traits. It also explains the rationale (protect caller's context). Missing details like error handling or timeout are minor for a query tool.

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?

The description is concise (three sentences) and well-structured: purpose and return format first, then usage tips. Every sentence earns its place, and the key information about preview-capping is front-loaded. No fluff or repetition.

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?

The tool has an output schema, so return format is partly covered, but the description still explains the markdown table and metadata. It covers the essential aspects: read-only, preview cap, and summarisation. It lacks info on error behavior or limits, but for a straightforward query tool this is acceptable.

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 description coverage is 0%, so the description must compensate. It explains max_rows' purpose (when to pass it) but does not describe the 'query' parameter beyond implying SELECT/WITH syntax. The query is self-explanatory as a string, but the parameter semantics are not fully fleshed out for a schema that offers no descriptions.

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?

The description clearly states the tool executes read-only SELECT/WITH queries and returns a markdown table with metadata. This distinguishes it from sibling tools like list_tables or sample_table_data, which serve different purposes. The verb 'executes' and resource 'query' are specific and unambiguous.

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?

The description provides practical usage guidance (e.g., aggregate in SQL, only pass max_rows when needed) but does not explicitly differentiate when to use this tool versus alternatives like sample_table_data. The context of being the primary query tool is implied, but not stated as a rule or recommendation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description must carry the behavioral burden. It does disclose that exact_row_count is optional and that statistics are returned by default, which is useful. However, it does not mention potential performance costs of exact_row_count, permission requirements, or error behavior. It adds some value but not the full transparency expected without annotations.

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?

The description is a single, efficient sentence that front-loads the main action and lists specific details. There is no fluff or repetition, and every word adds value.

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?

For a tool with two parameters and an output schema, the description is mostly complete. It specifies what data is returned and the optional flag. The only minor gap is the lack of notes on the cost or implications of exact_row_count, but this is not critical for basic use given the output schema exists.

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 description coverage is 0%, so the description must compensate. It explicitly explains that exact_row_count yields an exact count instead of statistics, which goes beyond the bare boolean type. table_name is self-evident from its name, so the description covers the key parameter semantics adequately.

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?

The description clearly states the verb 'Retrieves' and the specific resource 'table details', listing the exact fields (owner, tablespace, optimizer statistics, optional row count). This distinguishes it from siblings like get_table_schema (schema structure) and profile_table (data profiling). The purpose is unambiguous and not a tautology.

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?

The description gives no guidance on when to use this tool versus alternatives. It does not mention, for example, 'use this instead of get_table_schema when you need statistics' or any exclusions. The agent is left to infer usage purely from the tool's name and description, which is minimal.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description must disclose behavior. It states it returns a compact markdown table with specified fields, implying a read-only operation. However, it does not mention potential failure modes (e.g., nonexistent table), whether the result is ordered, or any access requirements. For a simple retrieval tool this is adequate but not rich.

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?

The description is a single, well-structured sentence that front-loads the action ('Retrieves the schema') and then details the output fields. There is no redundancy or filler; every clause earns its place.

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?

For a simple tool with one parameter and an existing output schema, the description is mostly complete. It tells what is returned and covers both tables and views. The only minor gap is not addressing error behavior or naming conventions, but these are not critical for a straightforward retrieval tool.

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?

The schema has no descriptions for the single parameter 'table_name', and the description does not compensate. It does not specify whether the name should be fully qualified, case-sensitive, or how to reference views versus tables. With 0% schema coverage, the description carries the full burden and fails to add any parameter-level meaning.

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?

The description clearly states the tool retrieves the schema of a table or view, and specifies the exact components returned (column name, data type, length, nullability, primary key). This is a specific verb-resource pair that distinguishes it from siblings like get_table_details (which likely covers more than schema) and list_tables (which lists tables). The markdown format is an extra clarifying detail.

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?

No guidance is provided on when to use this tool versus alternatives such as get_table_details or execute_query. There is no mention of prerequisites, limitations, or scenarios where another tool would be more appropriate. The agent must infer usage from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the burden of behavior disclosure. It communicates a read-only operation through 'Retrieves' and is open about the markdown output, but it doesn't mention permissions, auth needs, errors, or that no data is materially changed. This is acceptable for a listing tool but not rich.

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?

One sentence front-loads the core action and output format, then adds optional parameter detail. No filler redundancy or repeated title content.

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?

The tool is simple with one optional parameter and an output schema present. The description covers purpose, scope, output format, and parameter optionality, which is nearly complete. The main missing piece is explicit behavior for providing versus omitting schema name, but this is minor at this complexity.

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 0% and the description was at least notes 'Optionally takes a schema name', which clarifies that schema is optional and what it refers to. However, it does not explain how the optional schema interacts with the configured schema or list filtering, and the property name already indicates 'schema', so the description adds limited semantic value.

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?

The description clearly states the action ('Retrieves a list'), the resource ('all tables and views'), the context ('configured Oracle schema'), and the output format ('compact markdown table'). It distinguishes naturally from siblings like get_table_schema, which targets a single table, and execute_query, which runs ad-hoc SQL.

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?

The intended use in this case is implied by the purpose: use when you need to enumerate tables/views. The sibling tool names add context, but the description offers no explicit guidance on when to choose it over alternatives, or when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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, but it does not state whether the tool is read-only, how expensive it could be, or how it handles empty/missing data. It lists outputs, but important behavioral traits like modification safety, performance, or error conditions are left unstated.

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?

A single, tightly worded sentence packs a clear purpose and the core delivered statistics. There is no fluff—every word adds signal: it says what, what what purpose, and what kind of output.

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?

The description covers the high-level goal but does not explain the parameter semantics or usage boundaries, which matters because the schema descriptions are empty. Even though an output schema exists, the tool cannot be reliably invoked with just this description plus schema.

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 0%, so the description should compensate by explaining the three parameters. It only vaguely implies table_name via 'Profiles a table' and never explains columns or max_columns, including optionality, defaults, or effects. This is a critical gap for correct invocation.

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?

The description clearly states the tool's specific function: profiling a table for visualization with row count and per-column statistics to guide chart selection. It distinguishes the tool from siblings like sample_table_data or get_table_schema by emphasizing the analytical/statistical output and visualization intent.

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?

It gives clear context: this is used to determine an appropriate chart by inspecting table characteristics. It does not explicitly name alternatives or when not to use it, so it stops short of a 5, but the intended use case is sufficiently communicated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations are absent, so the description carries the full burden of behavioral disclosure. It discloses that only a small sample is returned, that rows come as a markdown table, and that sample_size defaults to 10 and is server-capped. However, it doesn't state whether the sample is deterministic/random, whether the operation is read-only (though 'retrieves' implies so), or how errors like non-existent tables or permission failures behave. Adequate but not complete.

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?

Two sentences, no filler. The main purpose and output format are front-loaded; the parameter behavior follows in the second sentence. Everything present contributes necessary information, and no word is wasted.

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 an output schema available and a simple two-parameter input, the description covers the core behavior, sample mechanism, output format, and the default/cap behavior of sample_size. It is complete enough for an agent to successfully call the tool for a quick preview. The only missing-piece is a clarification about how this differs from execute_query when both could return rows, but that is more of an usage guideline than a completeness gap.

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 description coverage is 0%, so the description must compensate. It clearly defines sample_size's effect ('controls how many rows to return'), the default (10), and server cap. It correctly identifies table_name as 'the specified table'. This adds meaningful semantics beyond the bare integer/string types. Slight gap: it does not specify table_name formatting or whether sample_size is inclusive or capped at a hard maximum.

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?

The description states a clear, specific action ('Retrieves a small sample of rows'), names the resource ('from the specified table'), and specifies the output format ('compact markdown table'). This is distinct from siblings like list_tables (lists table names), get_table_details (metadata), and profile_table (statistics); even execute_query is clearly different in scope ('a small sample' vs arbitrary query results). There is no ambiguity about what the tool does.

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?

The description implies usage for quick data previews but does not explicitly say when to choose this over execute_query or get_table_details. It states that sample_size is capped by the server, but it does not provide scenarios or compare to full-query retrieval, leaving the 'instead of' guidance to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.