Ophis

Official

by ophis-fi

Overview Schema Related Servers Score Discussions

Rust

Remote

Server Quality Checklist

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

Latest release: v0.0.1
Disambiguation5/5
Each tool has a distinct purpose: build_order constructs orders, get_quote fetches quotes, list_chains provides chain info, lookup_tier checks fee tiers, parse_intent interprets natural language, and submit_order relays signed orders. No overlap or ambiguity.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern (build_order, get_quote, list_chains, lookup_tier, parse_intent, submit_order), making them predictable and easy to navigate.
Tool Count5/5
With 6 tools, the server is well-scoped for its purpose of building and submitting CoW orders. Each tool serves a necessary role without redundancy, and the count is appropriate for the domain.
Completeness5/5
The tool set covers the full lifecycle: get a quote (get_quote), construct an order (build_order), and submit it (submit_order), plus auxiliary tools for chain info, fee tiers, and natural language parsing. No obvious gaps for the intended use case.
Average 4.4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 690 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 GPL 3.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
View related servers

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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It indicates a read-only operation ('Look up') and mentions returning live status and indexer status, implying no side effects. It does not disclose auth requirements or rate limits, but for a simple lookup, the core behavior is well-transparent.
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 consists of two concise sentences that front-load the main action ('Look up a wallet's Ophis fee-rebate tier') and summarize return values. Every part is essential with no waste.
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 low complexity (1 parameter, no output schema), the description covers the purpose, the volume-to-tier mapping, and the return components (indexer status and tier ladder). It does not detail error scenarios, but provides sufficient context for the tool's use.
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% as the only parameter 'wallet' is described in the schema. The description does not add any new meaning beyond the schema's 'Wallet address (0x...).' 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 the tool looks up a wallet's Ophis fee-rebate tier based on 30-day volume, specifying the tier progression and rebate percentage. It also mentions returning indexer status and the static tier ladder, which fully defines the tool's purpose and distinguishes it from sibling tools like build_order or get_quote, which have unrelated functions.
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 the tool should be used when a wallet's fee-rebate tier is needed, but it does not explicitly state when to use it versus alternatives. No exclusions or prerequisites are mentioned, making it adequate but not explicit.
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, the description bears full burden. It discloses the forwarding nature and keyless operation, but lacks details on failure behavior, error responses, or rate limits. The return value is mentioned succinctly.
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?
Two sentences, fully front-loaded, no redundant information. Every sentence contributes essential guidance.
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 and nested objects, the description covers the tool's purpose, input sources, and key constraint (pre-signed). It lacks examples or error scenarios, but is sufficient for an agent familiar with the domain.
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 coverage is 100%, so baseline is 3. The description adds workflow context by linking parameters to build_order outputs ('Pass the exact order object and fullAppData from build_order'), which helps the agent understand the data flow beyond schema definitions.
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 relays a pre-signed order to the Ophis orderbook, specifying the exact inputs (order, fullAppData from build_order, signature, from). It differentiates from sibling tools like build_order and get_quote by explicitly noting the pre-signed requirement.
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 explains that inputs must come from build_order and that the tool only forwards (holds no keys). However, it does not explicitly state when to use this tool vs alternatives or provide exclusions, leaving the agent to infer the workflow order.
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 must disclose behavior. It mentions the output structure and the live parser backend, providing transparency about the tool's operation. However, it does not discuss error handling, rate limits, or side effects, which are minor for a parsing tool.
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 concise with two sentences, no unnecessary information. It is front-loaded with the purpose and includes an example, making it efficient for an agent to quickly understand the tool.
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 tool with one parameter and no output schema, the description is fairly complete. It explains input, output structure, and backend. It could mention when to use it in a workflow, but overall it provides sufficient context.
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 parameter schema has 100% coverage with a description, but the tool's description adds value by providing an example of acceptable input text and clarifying the expected format. This goes beyond the schema alone.
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: parsing plain-English swap requests into structured intents. It provides a concrete example and defines the output format, distinguishing it from sibling tools like build_order or get_quote.
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 converting natural language to intents, but does not explicitly state when to use it over alternatives or provide exclusions. Sibling tools have different purposes, so some context is present but not explicit.
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 full burden. It discloses key behaviors: amounts are in atoms (uint256 decimal string), kind semantics for sell vs buy, return fields (sellAmount/buyAmount/feeAmount/validTo), and slippage instructions. It could explicitly state it is a read-only operation, but the 'Fetch' verb implies that. Overall, it provides substantial transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph of 4 sentences, each earning its place. It starts with the primary purpose, then covers units, kind, return values, and follow-up action (slippage). It is well-structured and sufficiently concise, though a minor reorganization could improve scannability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking an output schema, the description explains the return fields (sellAmount/buyAmount/feeAmount/validTo) and provides crucial contextual links: using chainId from list_chains and applying slippage before build_order. For a 7-parameter tool with 6 required, it covers all essential information an agent needs to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, but the description adds significant meaning beyond the schema: it explains that amounts are in atoms (uint256 decimal string), clarifies kind semantics ('kind='sell' the amount is the sell amount before fee; kind='buy' it is the desired buy amount'), and states that validForSeconds defaults to 1200. This enriches the parameter understanding beyond what the schema provides.
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?
Description starts with a clear verb-resource pair: 'Fetch a best-execution quote from the chain's Ophis orderbook'. It specifies the resource (quote) and context (Ophis orderbook), and distinguishes from siblings by referencing list_chains for chainId and build_order for subsequent steps.
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 provides explicit usage context: 'use a chainId from list_chains' and 'Before build_order, apply slippage...'. While it does not list when not to use this tool, it implicitly differentiates from sibling tools like list_chains and build_order, offering sufficient guidance for an agent.
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?
No annotations provided, but description discloses that paused chains throw, includes non-canonical notes, and details contents of each chain. Does not cover idempotency or other behaviors, but sufficient for a read-only list tool.
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?
Packed with information but well-structured and front-loaded. Could be slightly more concise, but each sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without output schema, description fully explains output structure (tradeable vs paused, contents) and provides context on non-canonical chains and error behavior. Complete for this tool.
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?
No parameters, so baseline is 4. Description adds value by explaining output structure and behavior beyond what schema (which is empty) provides.
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?
Clearly states it lists Ophis chains, splitting into tradeable and paused, with specific details. Distinguishes from siblings by noting which chains should be used with get_quote/build_order.
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?
Explicitly says only route get_quote/build_order to tradeable chains and that paused chains throw errors. Provides clear guidance on when to use each category.
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?
No annotations provided, so description carries full burden. It discloses key behaviors: receiver always pinned, slippage enforced via live quote, rejection if limit worse or quote unavailable, correct per-chain contract, partner fee embedding. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Every sentence adds necessary information. Structure is logical: purpose first, then key constraint, then usage guidance, then next steps. No redundant or vague language.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (14 params, no output schema), the description is exceptionally thorough. It covers return value structure, behavioral constraints, parameter usage, and post-build steps. Missing only minor detail on referenceBuyAmount/referenceSellAmount, but overall complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is high (86%), but description adds significant meaning beyond schema, such as explaining slippage enforcement, the relationship between sellAmount/buyAmount and kind, and noting feeAmount must be omitted or '0'. This greatly aids parameter interpretation.
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?
Description clearly states the tool builds a bounded, ready-to-sign CoW order on Ophis, with specific return fields. It distinguishes itself from sibling tools like get_quote and submit_order by focusing on order construction.
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?
Provides explicit guidance on when to use (e.g., for building an order) and how to use (e.g., slippage application, fee handling, signing steps). Implicitly tells when not to use (e.g., for quotes use get_quote). Also mentions the receiver is always pinned, no custom-receiver option.
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:

[![ophis MCP server](https://glama.ai/mcp/servers/ophis-fi/ophis/badges/card.svg)](https://glama.ai/mcp/servers/ophis-fi/ophis)

Score Badge

Copy to your README.md:

[![ophis MCP server](https://glama.ai/mcp/servers/ophis-fi/ophis/badges/score.svg)](https://glama.ai/mcp/servers/ophis-fi/ophis)

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/ophis-fi/ophis'

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