Fermat MCP

by abhiphile

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
The tools are grouped by library (mpl, numpy, sympy) with clear distinctions between groups, but within groups there is significant overlap. For example, mpl_mcp_plot_chart can plot line/scatter/bar charts, making mpl_mcp_plot_barchart and mpl_mcp_plot_scatter partially redundant. Similarly, numpy_mcp_numerical_operation includes matrix operations that overlap with numpy_mcp_matlib_operation, and sympy tools have some functional overlap (e.g., equation solving appears in multiple places). Descriptions help, but agents may struggle to choose between overlapping tools.
Naming Consistency4/5
Naming follows a consistent pattern of library_prefix_mcp_domain_operation (e.g., mpl_mcp_plot_chart, numpy_mcp_matlib_operation). All tools use snake_case consistently. Minor deviations exist, such as 'matlib' vs 'matrix' in numpy tools and slight variations in domain terms (e.g., 'algebra_operation' vs 'calculus_operation'), but the overall structure is predictable and readable.
Tool Count4/5
With 12 tools, the count is reasonable for a mathematical/plotting server covering multiple libraries (matplotlib, numpy, sympy). It's slightly on the higher side but justified by the broad scope. Each tool groups related functionalities, though some consolidation might reduce overlap. The count aligns well with the server's purpose of providing mathematical operations and visualizations.
Completeness4/5
The server covers key mathematical domains: plotting (multiple chart types), numerical operations (basic and advanced), symbolic algebra, calculus, and matrix operations. There are minor gaps, such as no explicit tool for statistical distributions beyond basic stats in numpy, and plotting tools might lack 3D or specialized visualizations. However, core workflows for mathematical analysis and visualization are well-supported, with no major dead ends.
Average 2.3/5 across 12 of 12 tools scored. Lowest: 1.7/5.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 6 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
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 but provides almost none. 'Plots mathematical equations' doesn't indicate whether this generates visual output, returns data, saves files, or has side effects. The 15 parameters suggest complex behavior, but the description gives no insight into what the tool actually produces, how it handles errors, whether it's idempotent, or any performance characteristics. For a tool with this many parameters and no annotations, the description is completely inadequate.
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 maximally concise at just two words. While this represents severe under-specification rather than ideal conciseness, from a pure structural perspective, there's no wasted language or unnecessary elaboration. Every word ('Plots mathematical equations') directly addresses the tool's function, making it technically efficient if inadequate in content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is completely inadequate given the tool's complexity. With 15 parameters, no annotations, no output schema, and 11 sibling tools, the two-word description fails to provide the necessary context for an agent to understand when and how to use this tool effectively. It doesn't explain what the tool produces, how it differs from alternatives, what the parameters mean, or any behavioral characteristics. This is a high-complexity tool with minimal description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 15 parameters and 0% schema description coverage, the description provides zero information about any parameters. The schema has titles but no descriptions, so parameters like 'equations', 'x_min', 'figsize', 'save', etc. have no semantic explanation. The description doesn't mention any parameters, their purposes, or how they interact. For a tool with this many undocumented parameters, the description fails completely to compensate for the schema coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Plots mathematical equations' is a tautology that essentially restates the tool name 'mpl_mcp_eqn_chart'. While it indicates the general domain (plotting equations), it lacks specificity about what distinguishes this tool from its many siblings, particularly other plotting tools like 'mpl_mcp_plot_chart' or equation-related tools like 'sympy_mcp_equation_operation'. The description doesn't clarify if this is for 2D plots, what types of equations it handles, or how it differs from other visualization tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides absolutely no guidance on when to use this tool versus alternatives. With 11 sibling tools including multiple plotting tools (barchart, scatter, stack, stem, general chart) and equation operation tools, the agent has no information about when this specific equation plotting tool is appropriate versus other visualization or mathematical tools. There's no mention of prerequisites, typical use cases, or comparison to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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 but provides none. It doesn't mention that this creates a visualization output, what format it returns (image file? display?), whether it has side effects, or any performance considerations. The description only states what it does, not how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single 6-word sentence. While this is efficient, it's arguably under-specified given the tool's complexity. However, it's front-loaded with the core purpose and contains no wasted words, earning a high conciseness score despite the content being insufficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex plotting tool with 16 parameters, no annotations, no output schema, and 0% schema description coverage, the description is completely inadequate. It doesn't explain what the tool returns, how to interpret parameters, when to use it, or any behavioral characteristics. The description fails to provide the context needed for effective tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 16 parameters and 0% schema description coverage, the description provides no parameter information beyond the tool name. The schema has titles like 'X Data' and 'Y Data' but no descriptions. The tool description doesn't explain what x_data and y_data represent, what formats are acceptable, or how the 14 optional parameters affect the visualization.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Plots stem chart of given datavalues' clearly states the verb ('plots') and resource ('stem chart'), but it's vague about what a stem chart is and doesn't differentiate from sibling plotting tools like mpl_mcp_plot_barchart or mpl_mcp_plot_scatter. It provides basic purpose but lacks specificity about the visualization type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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. With multiple sibling plotting tools (barchart, scatter, stack, etc.), there's no indication of when a stem chart is appropriate versus other visualization types. No usage context, prerequisites, or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. The description reveals nothing about whether this creates visual output, saves files, requires specific dependencies, has performance characteristics, or what happens when invoked. 'Plots' is the only behavioral clue, which is insufficient for a tool with 9 parameters.
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 at just 5 words. While this represents under-specification rather than ideal conciseness, it contains zero wasted words and is front-loaded with the core action. Every word earns its place, even if more content is needed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 9-parameter plotting tool with no annotations, no output schema, and multiple sibling tools, the description is completely inadequate. It doesn't explain what the tool produces (visual display? file output?), how to interpret parameters, when to use it, or any behavioral characteristics. The agent would struggle to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 9 parameters (8 optional), the description provides no information about any parameters. It doesn't mention the required 'values' array, optional labels, title, axis labels, color, save option, DPI, or orientation. The schema must carry all parameter documentation burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Plots barchart of given datavalues' states the basic action (plots) and resource (barchart), but is vague about scope and lacks differentiation from sibling tools like 'mpl_mcp_plot_chart' or 'mpl_mcp_plot_stack'. It doesn't specify what makes this barchart tool unique among the various plotting tools available.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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. With multiple sibling plotting tools (plot_chart, plot_scatter, plot_stack, plot_stem), the description offers no context about when a barchart is appropriate or when other chart types might be better suited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description offers no behavioral information. It doesn't mention that this is a computational tool, whether operations are performed in-memory, what the output format is, error handling, performance characteristics, or any constraints. The description is purely a list of operation names without context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While technically concise as a single run-on list, the structure is poor - it's just a comma-separated enumeration without proper sentence structure or organization. The information isn't front-loaded with purpose; it jumps straight to listing operations without context.
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?
For a complex tool with 12 parameters, 0% schema coverage, no annotations, but with an output schema, the description is severely inadequate. It doesn't explain the tool's scope, parameter usage, or behavioral characteristics. The output schema helps with return values, but the description doesn't provide the necessary context for proper tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 12 parameters and 0% schema description coverage, the description provides zero information about parameters. It doesn't explain what 'operation' expects (the listed operations), how 'a' and 'b' relate to operations, what 'shape' and 'new_shape' do, or how other parameters like 'axis', 'q', 'start/stop/step' are used. The description fails to compensate for the complete lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description lists operations but is vague about what the tool actually does - it doesn't specify that it performs numerical operations on arrays/numbers using NumPy. While it distinguishes from plotting siblings, it doesn't clearly differentiate from other numerical/matrix operation siblings like numpy_mcp_matlib_operation or sympy_mcp_matrix_operation.
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?
No guidance on when to use this tool versus alternatives. The description doesn't mention NumPy specifically, provide context about numerical vs symbolic computation, or indicate when to choose this over sibling tools like sympy_mcp_algebra_operation or numpy_mcp_matlib_operation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. However, it only lists operation types without explaining what the tool does (e.g., computes derivatives, integrals, etc.), expected outputs, error conditions, or performance traits. This is inadequate for a tool with 9 parameters and complex operations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's front-loaded with the core purpose, though it could be more structured by separating operation examples. The brevity is appropriate but under-specified.
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 tool's complexity (9 parameters, calculus operations), no annotations, and 0% schema coverage, the description is incomplete. While an output schema exists, the description lacks essential context like input formats, mathematical assumptions, or error handling. It doesn't provide enough information for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning parameters like 'expr', 'sym', 'n', 'lower', 'upper', 'point', 'direction', and 'series_n' are undocumented in the schema. The description adds no meaning beyond the operation names, failing to compensate for the coverage gap. For example, it doesn't clarify that 'expr' is a mathematical expression string or how parameters interact.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Do calculus operations like diff, integrate, limit, series' states the general purpose (calculus operations) and lists examples, but it's vague about the specific resource (mathematical expressions) and doesn't distinguish from sibling tools like sympy_mcp_algebra_operation or sympy_mcp_equation_operation. It provides a basic idea but lacks specificity.
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?
No guidance is provided on when to use this tool versus alternatives. The description lists operations but doesn't mention prerequisites, when to choose this over other sympy tools, or any exclusions. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. However, it only lists operation names without explaining what they do (e.g., solve for solutions, solveset for solution sets, linsolve for linear systems, nonlinsolve for nonlinear systems), their effects (e.g., returns symbolic expressions, may raise errors), or any constraints (e.g., performance, input formats). This leaves critical behavioral traits like output format, error handling, and computational limits completely unspecified.
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, consisting of a single sentence that directly states the tool's purpose with no wasted words. It is front-loaded and efficiently communicates the core functionality, making it easy to scan. However, this conciseness comes at the cost of completeness, as noted in other dimensions.
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 (10 parameters, no annotations, 0% schema coverage) and the presence of an output schema, the description is incomplete. While the output schema may cover return values, the description lacks essential details: it doesn't explain the operations, parameter meanings, usage context, or behavioral traits. For a tool with many parameters and no annotation support, this minimal description is inadequate to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 10 parameters and 0% schema description coverage, the schema provides no descriptions for any parameters. The description adds no information about what parameters like 'equations', 'symbols', 'domain', or flags such as 'check' and 'simplify' mean, their expected formats, or how they interact with operations. This fails to compensate for the lack of schema documentation, leaving parameters largely unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool performs symbolic equation operations and lists four specific operations (solve, solveset, linsolve, nonlinsolve), which provides a general purpose. However, it lacks specificity about what these operations do (e.g., solving equations vs. other symbolic manipulations) and doesn't clearly distinguish from sibling tools like sympy_mcp_algebra_operation or sympy_mcp_calculus_operation, which may have overlapping functionality. The phrase 'like solve, solveset, linsolve, nonlinsolve' is somewhat vague, as it doesn't define the scope beyond listing examples.
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 any context, prerequisites, or exclusions, such as when to choose this over sibling tools like sympy_mcp_algebra_operation or sympy_mcp_calculus_operation, or what types of equations are suitable. Without such information, users must infer usage from the operation names alone, which is insufficient for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 plotting but doesn't describe what happens (e.g., creates a file, displays to screen, returns image data), whether it's read-only or has side effects, performance characteristics, or error conditions. This is inadequate for a 13-parameter tool with no annotation coverage.
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 at just one sentence with zero wasted words. It's front-loaded with the core functionality and uses efficient terminology. While it's under-specified, it's not verbose or poorly structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex 13-parameter plotting tool with no annotations, no output schema, and 0% schema description coverage, this description is completely inadequate. It doesn't explain what the tool returns, how parameters interact, what format the output takes, or any behavioral characteristics needed for proper invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning all 13 parameters have only titles in the schema. The description mentions 'datavalues' but provides no explanation of what x_data and y_data should contain, the difference between area and bar chart types, or how other parameters like colors, alpha, or figsize affect the output. The description fails to compensate for the complete lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool creates stacked area/bar charts from data values, which is a clear purpose. However, it doesn't differentiate from sibling tools like mpl_mcp_plot_barchart or mpl_mcp_plot_chart, leaving ambiguity about when to choose this specific stacked visualization tool.
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?
No guidance is provided about when to use this tool versus alternatives. With multiple plotting siblings (mpl_mcp_plot_barchart, mpl_mcp_plot_chart, etc.), the description offers no comparison or context for selecting this stacked chart option over other visualization tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but provides minimal behavioral information. It states what the tool does but doesn't disclose whether it creates visual output, saves files, requires specific data formats, or has any side effects. 'Plots' implies visual generation, but details about output format, display behavior, or file handling are missing.
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?
Extremely concise single sentence with zero wasted words. The description is front-loaded with the core purpose. While it's under-specified, what's present is efficiently structured without redundancy.
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?
For a complex tool with 17 parameters, 0% schema description coverage, no annotations, and no output schema, the description is severely inadequate. It identifies the chart type but provides no guidance on parameter usage, output behavior, error conditions, or relationship to sibling tools. The agent would struggle to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
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 but fails completely. The description mentions 'given datavalues' which vaguely references x_data and y_data parameters, but provides no explanation for any of the 17 parameters, their purposes, relationships, or how they affect the scatter plot. This leaves most parameters semantically undocumented.
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 tool's purpose: 'Plots scatter chart of given datavalues' - a specific verb ('plots') and resource ('scatter chart'). It distinguishes from some siblings (e.g., mpl_mcp_plot_barchart, mpl_mcp_plot_stack) by specifying scatter chart type, but doesn't fully differentiate from mpl_mcp_plot_chart which could be ambiguous.
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?
No guidance on when to use this tool versus alternatives. The description doesn't mention when scatter plots are appropriate versus other chart types (bar, stack, stem) available in sibling tools, nor does it provide any context about prerequisites 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.
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 mentions what operations are available but doesn't describe what these operations do, their mathematical effects, error conditions, performance characteristics, or output format. For a tool with 18 parameters and complex mathematical operations, this is a significant gap.
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 purpose. There's no wasted space or unnecessary elaboration. It's appropriately sized for what it does convey, though it's under-specified rather than concise.
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 (18 parameters, mathematical operations), lack of annotations, and 0% schema description coverage, the description is severely incomplete. While an output schema exists (which helps with return values), the description doesn't provide enough context about when to use which operation, what the parameters mean, or the mathematical behavior of the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and 18 parameters (only 2 required), the description provides absolutely no information about parameters. It doesn't explain what 'expr' should contain, what 'syms' represents, or what any of the 16 optional parameters control. This fails to compensate for the complete lack of schema documentation.
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 tool performs algebraic operations and lists specific operations (simplify, expand, factor, collect), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like sympy_mcp_calculus_operation or sympy_mcp_equation_operation, which likely handle related mathematical 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 when to choose algebraic operations over calculus or equation operations from sibling tools, nor does it specify prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 mentions plotting but doesn't explain what happens (e.g., displays plot, saves file, returns image data), whether it's interactive, what permissions are needed, or any side effects. For a tool with 13 parameters and no annotation coverage, this is a significant gap.
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 with a single, efficient sentence that front-loads the core functionality. There's no wasted verbiage, though this brevity comes at the cost of completeness.
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 (13 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns (plot display, file path, image data), how to interpret parameters, or behavioral details. This is inadequate for a multi-parameter plotting tool with specialized siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 for all 13 parameters. It only mentions 'datavalues' (implied x_data and y_data) and chart types (plot_type), ignoring 11 other parameters like labels, title, save, dpi, etc. This leaves most parameters undocumented and their purposes unclear.
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 ('plots') and resource ('chart of given datavalues'), specifying the chart types (line/scatter/bar). However, it doesn't explicitly differentiate from sibling tools like mpl_mcp_plot_barchart or mpl_mcp_plot_scatter, which appear to be specialized versions of this general plotting tool.
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?
No guidance is provided about when to use this tool versus the specialized sibling tools (mpl_mcp_plot_barchart, mpl_mcp_plot_scatter, etc.). The description doesn't mention any prerequisites, context for usage, or alternatives, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It lists operations but doesn't explain what each operation does, their side effects (e.g., whether they create new matrices or modify existing ones), error conditions, or output format. For a tool with 11 parameters and no annotation coverage, this is a significant gap in behavioral 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 listing operations—with zero wasted words. It's front-loaded with the purpose and efficiently enumerates supported operations, making it easy to scan.
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 (11 parameters, no annotations, 0% schema coverage) and the presence of an output schema, the description is incomplete. It doesn't explain how operations map to parameters, what inputs are needed for each operation, or behavioral traits. While the output schema might cover return values, the description lacks crucial context for proper tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning none of the 11 parameters have descriptions in the schema. The description lists operations but doesn't explain which parameters correspond to which operations or their meanings (e.g., what 'm', 'n', 'k' represent). It adds minimal value beyond the schema, failing to compensate for the coverage gap.
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 tool performs 'matrix operations' and lists specific operations (rand-mat, zeros, ones, etc.), providing a specific verb+resource. However, it doesn't explicitly distinguish this from sibling tools like 'sympy_mcp_matrix_operation' or 'numpy_mcp_numerical_operation', which likely handle similar mathematical 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. There are multiple sibling tools (e.g., sympy_mcp_matrix_operation, numpy_mcp_numerical_operation) that might overlap in functionality, but the description offers no comparison, prerequisites, or context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions 'symbolic matrix operations' which implies mathematical computation, but doesn't disclose behavioral traits like error handling, performance characteristics, whether operations are read-only or mutating, or what the output format looks like. The description is too minimal for a tool with 6 parameters.
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 with a single sentence that efficiently lists the key operations. It's front-loaded with the core purpose and wastes no words, though this conciseness comes at the cost of completeness.
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?
For a tool with 6 parameters, 0% schema description coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain parameter usage, return values, error conditions, or behavioral expectations. The minimal description fails to provide necessary context for proper tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 lists operation types but doesn't explain what 'data' parameter should contain, what 'rational' and 'simplify' flags do, or when 'nrows' and 'ncols' are needed. The description adds minimal value beyond the schema's enum for 'operation'.
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 tool performs symbolic matrix operations and lists specific operations (create, det, inv, rref, eigenvals), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like sympy_mcp_algebra_operation or numpy_mcp_matlib_operation, which might also handle matrix 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 like sympy_mcp_algebra_operation or numpy_mcp_matlib_operation. It lists operations but doesn't specify context, prerequisites, or exclusions for usage.
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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