DeFi Guard MCP

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.2.0
Disambiguation5/5
Each tool addresses a distinct aspect of DeFi safety: position health, swap quoting, token liquidity, token safety, contract capabilities, and approval risk. There is no meaningful overlap; even the two token-focused tools (token_risk_snapshot and token_safety_screen) target different concerns (market depth vs. trade-ability/ownership).
Naming Consistency4/5
All tool names use lowercase with underscores and follow a compound noun or verb-noun structure. Most start with a noun (aave_position_health, token_risk_snapshot, approval_risk), while two start with verbs (quote_swap, scan_dangerous_capabilities), which is a minor inconsistency but not confusing.
Tool Count5/5
With 6 tools, the server is tightly scoped to its DeFi guard purpose. Each tool covers a core risk area and none feel redundant or excessive. This is an ideal size for the stated domain.
Completeness4/5
The tool set covers major DeFi risk vectors: position health, swap pricing, token liquidity, honeypot/tax checks, dangerous contract capabilities, and approval exposure. A minor gap is the lack of a comprehensive transaction simulation or portfolio-level risk assessment, but the core safety workflows are well supported.
Average 4.2/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 10 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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.
Add a glama.json file to provide metadata about your server.
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It adds useful details like trying all fee tiers and returning the best executable output, and specifies human units. However, it does not disclose whether the call is read-only, what happens on failure (e.g., no liquidity), or the exact return format beyond 'best executable output'.
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 two sentences, front-loaded with the core action, and includes only relevant specifics (fee tiers, human units). No fluff or redundancy.
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?
The tool has no annotations and no output schema, so the description must fully cover behavior. It explains the quoting mechanism and human units but leaves out the return format (e.g., amountOut, fee tier chosen) and error scenarios. Given the moderate complexity, the description is adequate but incomplete.
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 coverage is 100% with descriptions like 'Input token address' and 'Human-readable input amount, e.g. '0.5''. The description reinforces 'Amounts are human units' but adds no additional meaning beyond the schema for any parameter. Baseline of 3 is appropriate.
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 uses a specific verb ('quote') and names the exact resource ('swap on Base via Uniswap V3 QuoterV2'). It clearly distinguishes from sibling tools (which focus on risk/health) by focusing on quote generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies clear usage: use when you need an exact-input swap quote on Base. It does not explicitly state when not to use or name alternatives, but the context is unambiguous given the sibling tools are unrelated.
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, the description carries the behavioral burden. It discloses the output metrics and clarifies that a health factor below 1.0 means liquidatable, which is valuable interpretive context. It does not mention potential errors or data sources, but the core behavior is well described.
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 two sentences, front-loaded with the action, and concisely lists the key metrics and their interpretation. No extraneous information is present.
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 simple input schema and no output schema, the description adequately explains what the tool returns and what the health factor means. It is complete enough for an agent to invoke and interpret results, though it does not mention edge cases like positions with no collateral.
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 coverage is 100% for the single address parameter, and the schema already describes it as 'EVM address of the position owner.' The description adds no additional parameter-level detail, so it meets the baseline but does not exceed 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 checks Aave V3 position health on Base, listing specific metrics (health factor, collateral/debt, LTV, liquidation threshold). This specific verb+resource distinguishes it from sibling tools like quote_swap or token_risk_snapshot.
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 monitoring liquidation risk but does not explicitly state when to use it versus alternatives or provide exclusions. No sibling tool is mentioned for comparison, so guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description fully explains the heuristic method (4-byte selector matching), the meaning of results (presence of capability, not actual operation), and the proxy limitation (logic hidden in implementation is reported). It also mentions no API key is needed, giving a complete behavioral picture.
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?
Three sentences efficiently pack purpose, semantics, heuristic, and limitations without filler. Each clause adds value, though slightly dense for a quick read.
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?
Covers what is scanned, specific capabilities, heuristic meaning, proxy limitation, and operational detail (no API key). It does not specify exact output structure, but 'flags presence' is sufficient for a simple single-parameter tool with no output schema.
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?
The schema already describes the single parameter fully ('Contract/token address on Base'), and the description adds no additional format, validation, or usage detail beyond the schema. Baseline of 3 applies due to high schema coverage.
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 scans deployed bytecode for owner-only powers (mint, pause, blacklist, etc.) using a specific verb and resource. It differentiates from sibling risk/safety tools by focusing specifically on selector-based capability detection, independent of token claims.
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?
Implies usage for verifying a token's actual on-chain capabilities versus its stated claims, but no explicit when-to-use or when-not-to-use guidance is given. It does not reference alternative sibling tools like token_safety_screen, leaving the choice implicit.
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, the description discloses that market depth is 'REAL' and measured by round-trip quotes (WETH -> token -> WETH) at two trade sizes, indicating it uses quotes rather than executing trades. It also explains the interpretation of high round-trip loss, adding behavioral context beyond a simple snapshot.
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 two sentences, front-loaded with the tool's purpose, then adds the key measurement methodology and interpretation. Every sentence contributes meaningful information.
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?
For a simple one-parameter, no-output-schema tool, the description sufficiently outlines what it returns (metadata plus round-trip quotes at two sizes) and how to interpret it. It could specify the exact metadata fields or trade sizes, but these details are not essential for a basic understanding.
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?
The schema already fully describes the single parameter 'token' as an ERC-20 token address on Base. The description repeats this context without adding syntax, format, or edge-case details, so it adds no additional semantic value beyond the 100% schema coverage.
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 it produces a liquidity risk snapshot for an ERC-20 on Base, using round-trip quotes to measure market depth. It distinguishes from siblings by focusing specifically on illiquidity risk rather than safety, approvals, or quote conversion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when assessing whether a token can be exited, noting high round-trip loss indicates thin/illiquid or high-fee markets. It provides context for when to use (risk assessment) but does not explicitly name alternative tools or exclusion criteria.
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, the description carries the full burden and does well by disclosing it reads live allowance, flags unlimited approvals, and reports contract status. It also explains the unlimited-allowance drain vector and EOA scam setup, adding useful behavioral context, though it omits return format or explicit read-only confirmation.
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?
Three dense, well-structured sentences lead with the core purpose, unpack behavior via a colon, and end with concrete use cases. Every sentence earns its place with no filler or repetition.
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?
Despite no output schema, the description covers the key outputs (flags unlimited approvals, reports contract status) and provides purpose, chain, and use cases. It does not detail return shape or error behavior, but enough context is present for an agent to select and invoke the tool correctly.
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 already describes all three parameters with 100% coverage, but the description adds risk-specific meaning by tying owner/token/spender to the allowance-drain vector and describing spender as a router, contract, or EOA. This enriches the bare field names and helps correct usage.
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 identifies the action (assess risk), resource (existing ERC-20 approval on Base), and key behaviors (reads live allowance, flags unlimited approvals, reports spender contract status). This distinguishes it from sibling tools focused on swaps, token risk, or position health.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use: before signing an approval or to audit standing approvals. It does not name alternative tools or exclusions, but the use cases are unambiguous and clearly differentiate it from the listed siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that the tool verifies sell-back ability, measures round-trip costs, checks ownership renunciation, returns a single risk verdict, and is on-chain only with no oracle or trust in token claims. This is robust behavioral disclosure.
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 front-loaded with the core purpose, and each sentence adds substantive value: what it checks, the details of the checks, the output, and the method. There is no 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 a single-parameter tool with no output schema and no annotations, the description is largely complete: it explains what it does, the input, and the output concept. A minor gap is lack of differentiation from sibling tool token_risk_snapshot, but overall the agent has enough context to select and invoke the tool.
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?
The input schema already documents the 'token' parameter as 'ERC-20 token address on Base' with 100% coverage. The description reinforces the parameter's role but adds no additional semantic detail beyond the schema, so the baseline of 3 applies.
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 it is a 'pre-trade safety screen for an ERC-20 on Base' and enumerates specific checks (honeypot detection, round-trip cost, ownership renounced). This verb+resource+scope formulation distinctly separates it from sibling tools like quote_swap or approval_risk.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear context for when to use: the 'is this safe to buy/approve BEFORE I sign' check. However, it does not explicitly name alternative tools or exclusion criteria, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![defi-guard-mcp MCP server](https://glama.ai/mcp/servers/iinniitt/defi-guard-mcp/badges/card.svg)](https://glama.ai/mcp/servers/iinniitt/defi-guard-mcp)

Score Badge

Copy to your README.md:

[![defi-guard-mcp MCP server](https://glama.ai/mcp/servers/iinniitt/defi-guard-mcp/badges/score.svg)](https://glama.ai/mcp/servers/iinniitt/defi-guard-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/iinniitt/defi-guard-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server