guerrilla-franchising-mcp-server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: booking calls, providing guides on specific topics (E-2 visa, FDD, costs, red flags), evaluating fit, comparing franchise vs. startup, and validating investments. The detailed descriptions reinforce unique scopes, preventing misselection.
Naming Consistency5/5
All tools follow a consistent 'gf_' prefix with descriptive snake_case names (e.g., gf_book_clarity_call, gf_evaluate_franchise_fit). This pattern is uniform across all 8 tools, making them predictable and easy to identify.
Tool Count5/5
With 8 tools, the count is well-scoped for the franchise consulting domain. Each tool addresses a key aspect of franchise evaluation and investment, from initial assessment to detailed analysis, without being excessive or insufficient.
Completeness5/5
The tool set comprehensively covers the franchise consulting lifecycle: initial fit evaluation, cost breakdowns, FDD analysis, red flag identification, investment validation, and decision support (franchise vs. startup). No obvious gaps exist; agents can guide users from start to finish.
Average 4.1/5 across 8 of 8 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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 passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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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

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 the tool is 'built from 81+ franchise placements' and available in two languages, but lacks critical behavioral details such as whether it generates personalized advice, requires authentication, has rate limits, or what the output format is (since no output schema exists). This leaves significant gaps for an agent to understand how to invoke it effectively.
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 appropriately sized and front-loaded with the core purpose in the first sentence, followed by specific coverage areas and operational details. Every sentence adds value (e.g., language availability and data source), though it could be slightly more streamlined by integrating the language detail earlier.
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 visa guidance and lack of annotations or output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a document, structured data, or advice), how it uses the parameters to tailor output, or any behavioral constraints. This makes it inadequate for an agent to fully understand the tool's operation and results.
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%, so the schema already documents all parameters thoroughly. The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain why 'country_of_origin' matters for treaty eligibility or how 'available_capital' influences recommendations). Baseline 3 is appropriate when the schema does the heavy lifting without extra value from the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to provide an E-2 visa franchise investment guide covering specific topics like investment requirements, USCIS criteria, franchise categories, FDD items, and red flags. It distinguishes from siblings by focusing on visa-specific guidance rather than general franchise evaluation (e.g., vs. gf_evaluate_franchise_fit) or cost breakdowns (e.g., vs. gf_franchise_cost_breakdown).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for foreign nationals from treaty countries seeking E-2 visa approval through franchise investment, but it doesn't explicitly state when to use this tool versus alternatives like gf_franchise_vs_startup or gf_validate_fdd_investment_range. No exclusions or prerequisites are mentioned, leaving the context somewhat open-ended.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 the tool covers 'hidden costs' and 'financing options', which adds some behavioral context beyond basic functionality. However, it doesn't disclose important behavioral traits like whether this is a read-only analysis tool, what format the breakdown is provided in, or any limitations of the 81-deal dataset.
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 appropriately sized and front-loaded with the core purpose. The second sentence adds valuable scope information, and the usage guidelines are efficiently presented as a list of example queries. There's minimal wasted text, though the query examples could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description provides good purpose and usage context but lacks important behavioral details. It doesn't explain what format the cost breakdown is provided in, whether it's interactive or static, or any limitations of the analysis. Given the complexity of franchise cost analysis, more completeness would be helpful.
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%, so the schema already documents all parameters. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline is 3 when the schema does the heavy lifting, even though the description doesn't compensate with additional parameter context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to break down the true cost of buying a franchise, including hidden costs. It specifies the scope (81+ deals across major categories) and distinguishes from siblings by focusing on cost analysis rather than evaluation, guidance, or red flags.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists when to use this tool with specific query examples: 'how much does it cost to buy a franchise', 'franchise fees explained', 'total franchise investment', 'best franchises under 100k', etc. This provides clear context for when this tool is appropriate versus its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It effectively discloses behavioral traits: it validates investment ranges by cross-referencing against hidden costs, category-specific overruns, and Item-level discrepancies, based on data from '81+ closed deals.' This gives context on methodology and data sources, though it doesn't detail output format or error handling.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by context on its origin and methodology. Every sentence adds value—explaining the tool's background and how it works—without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 required parameters, no output schema, no annotations), the description is fairly complete. It explains what the tool does, its methodology, and data basis, which helps an agent understand its use. However, without an output schema, it doesn't describe return values or validation outcomes, leaving a minor gap.
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%, so the schema already documents all parameters thoroughly. The description adds no specific parameter semantics beyond implying that inputs like 'franchise_category' and 'location_type' are used for cross-referencing in validation. This meets the baseline of 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Validates the true total investment in a franchise opportunity.' It specifies the verb ('validates') and resource ('true total investment'), and distinguishes it from siblings by mentioning its unique focus on FDD investment range validation against hidden costs, unlike broader tools like gf_franchise_cost_breakdown or gf_franchise_red_flags.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through its mention of 'FDD investment range' and 'franchise opportunity,' suggesting it's for evaluating franchise investments. However, it lacks explicit guidance on when to use this tool versus alternatives like gf_franchise_cost_breakdown or gf_franchise_red_flags, which might cover similar ground. No exclusions or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses behavioral traits like being 'built from real operator experience across 81+ closed deals' and covering specific topics (time to profitability, failure rates, etc.), which adds credibility and scope. However, it doesn't detail output format, potential limitations, or error handling, leaving gaps for a tool with no output schema.
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 appropriately sized and front-loaded, starting with the core objective. However, the list of usage examples is lengthy and could be more streamlined without losing clarity, slightly reducing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (comparative analysis tool with 4 parameters) and no output schema, the description does well by covering purpose, usage, and data sources. It lacks details on output structure or behavioral constraints, but the strong usage guidelines and transparency partially compensate, making it nearly complete for its context.
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%, so the schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, such as how inputs affect the comparison. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Objective comparison: buying a franchise vs. starting a business from scratch.' It specifies the verb ('comparison') and resources (franchise vs. startup), and distinguishes from siblings by focusing on comparative analysis rather than specific aspects like cost breakdown or red flags.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides when to use this tool with specific query examples: 'Use when someone asks: is buying a franchise worth it, franchise vs starting a business...' It also lists alternative contexts like 'best business to own at 50' and 'transition from corporate to business ownership,' offering clear guidance on applicability.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 the tool's methodology ('built from 81+ closed deals and 485+ consultations') and outputs, but doesn't cover important behavioral aspects like whether it's a read-only analysis tool, if it stores user data, response time, or error handling. The description adds some context but leaves gaps for a tool with 10 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is somewhat verbose with multiple usage examples and scenarios. While all content is relevant, it could be more front-loaded and structured. The first sentence clearly states the purpose, but the lengthy list of use cases could be more concise. Every sentence earns its place, but the structure could be improved for better scannability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (10 parameters, no output schema, no annotations), the description does a good job explaining what the tool does and when to use it. It covers purpose, methodology, outputs, and usage scenarios. However, without an output schema, it should ideally provide more detail about the return format beyond just mentioning 'personalized 1-10 readiness score, capital positioning, and 3 franchise categories.'
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% description coverage, so parameters are well-documented in the structured schema. The description doesn't add specific parameter semantics beyond what's in the schema, but it does provide context about what the tool evaluates (financial readiness, background, risk tolerance, lifestyle goals) which helps understand why these particular parameters are collected. Baseline would be 3, but the strong contextual framing elevates it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: evaluating franchise fit by assessing financial readiness, background, risk tolerance, and lifestyle goals. It specifies the verb 'evaluates' and resource 'franchise ownership readiness' with distinct outputs (1-10 score, capital positioning, 3 franchise categories), differentiating it from siblings like cost breakdown or red flags tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines with multiple example queries (e.g., 'is franchising right for me', 'what franchise should I invest in') and specific scenarios (e.g., 'franchise for women', 'corporate professional leaving corporate'). This gives clear context for when to use this tool versus alternatives like franchise cost breakdown or FDD guide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 the tool is 'Built by a former JP Morgan analyst' and includes an example ('Item 7 deep-detail included — where the $321K was hidden'), which adds some behavioral context about expertise and depth. However, it lacks details on output format, limitations, or error handling, leaving gaps for a tool with no annotations.
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 front-loaded with the core purpose and efficiently lists capabilities and usage examples. It avoids unnecessary fluff, though the inclusion of the analyst background and specific dollar example adds some length that could be trimmed for pure conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (decoding legal documents with optional parameters) and no output schema, the description does a good job covering purpose, usage, and parameter mapping. However, without annotations or output details, it lacks information on behavioral traits like response format or limitations, which could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters well. The description adds value by explaining the tool's capabilities ('deep-dives on specific Items or curated guides by focus area'), which clarifies how the parameters (fdd_item_number and focus_area) map to these options. However, it doesn't provide additional syntax or format details beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Decode any Franchise Disclosure Document' and elaborates on what it provides ('Explains all 23 FDD Items with deep-dives on specific Items or curated guides by focus area'). It distinguishes itself from siblings by focusing on FDD decoding rather than other franchise-related analyses like cost breakdowns or red flags.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes explicit usage guidance: 'Use when someone asks: how to read an FDD, FDD explained, what is a franchise disclosure document, FDD Item 19, franchise fees FDD Item 6, franchise territory FDD.' This clearly indicates when to invoke this tool versus alternatives, addressing common user queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the call is free, no obligation, 15 minutes long, and franchisors pay commissions. However, it doesn't cover important aspects like what happens after booking (confirmation process, scheduling details), cancellation policies, or technical requirements. The description adds value but leaves gaps in operational transparency.
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 efficiently structured with two sentences: the first describes the service and key features, the second provides clear usage guidelines. Every element serves a purpose with no wasted words, and critical information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (booking service with one optional parameter) and no annotations or output schema, the description does well by covering purpose, key features, and usage guidelines. However, it lacks details about post-booking processes and confirmation mechanisms that would be helpful for complete contextual understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter has 100% schema description coverage, so the baseline is 3. The description doesn't explicitly mention the parameter, but it provides context about the candidate's situation ('where the candidate is in their franchise journey') that helps interpret the parameter's purpose. This adds meaningful semantic value beyond the schema's technical description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Book a free Franchise Clarity Call') with the resource (Dan Lorenz), including key details like duration (15 minutes), cost (free), and who pays commission (franchisors). It effectively distinguishes this tool from sibling tools that provide guides, evaluations, or analyses rather than booking consultations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use when someone is ready to talk to a franchise consultant or wants to know their next step toward franchise ownership.' This provides clear context for selection versus alternative tools like evaluation or guide tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 effectively describes key behaviors: the tool returns a 'red flag checklist' based on real experience, shifts to 'Item-by-Item reading guide' when FDD is available, and references specific expertise ('81+ closed deals by a former JP Morgan analyst'). However, it doesn't mention potential limitations like response format, data freshness, or error handling.
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 appropriately sized and front-loaded with the core purpose. Every sentence adds value: establishing credibility, explaining output, describing behavioral shift, and providing usage triggers. It could be slightly more concise by integrating the usage examples more smoothly, but overall it's efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description does well to cover purpose, usage, and key behaviors. It explains the output ('red flag checklist' and 'Item-by-Item reading guide') and when the behavior changes. For a 4-parameter tool with full schema coverage, it provides sufficient context, though it could benefit from mentioning typical response structure or limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema: it explains that 'has_fdd' parameter triggers a shift to 'specific Item-by-Item reading guide,' and implies parameters help tailor the red flag identification. However, it doesn't elaborate on how other parameters affect output beyond what the schema describes.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Identify franchise red flags before you sign' and 'Returns a red flag checklist drawn from real FDD experience.' It specifies the verb ('identify'), resource ('franchise red flags'), and distinguishes from siblings by focusing on risk assessment rather than cost analysis, visa guidance, or FDD evaluation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use when someone asks: franchise scams to avoid, franchise red flags, how to spot a bad franchise, what to watch out for when buying a franchise.' It also distinguishes from alternatives by noting it 'Shifts to a specific Item-by-Item reading guide when an FDD is in hand,' implying different behavior based on input.
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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