Stocks On Chain
Server Details
Read-only tokenized stock data: issuers, chains, contract addresses and corporate actions.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.2/5 across 6 of 6 tools scored.
Each tool targets a distinct purpose: metadata, single stock details, chain listing, events, issuers, and search. No overlap.
All tools follow a consistent verb_noun pattern (get_, list_, search_), making the API predictable.
With 6 tools, the server is well-scoped for its domain of tokenized stock data, covering core queries without bloat.
Covers individual stock details, search, chains, events, and issuers. Minor gap: no batch price or bulk data endpoint, but the manifest hints at a paid tier.
Available Tools
6 toolsget_manifestGet the agent tier termsAInspect
What this service offers a machine reader: the free keyless endpoints, the bulk dataset and its price if the paid tier is live, and the licence. No human reader is ever charged or asked to connect a wallet.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries full behavioral disclosure burden. It mentions the tool is free ('no human reader is ever charged or asked to connect a wallet'), but lacks details on rate limits, authentication, side effects, or other behavioral constraints.
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?
Two short sentences that front-load the key information about what the manifest contains. Every word contributes to the description, with no filler.
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?
With no output schema, the description must explain return values. It lists the contents (endpoints, bulk dataset, price, license) but does not describe the structure or format of the returned data, leaving some ambiguity for an AI agent.
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?
The tool has zero parameters, so the description does not need to add parameter semantics. Baseline of 4 is appropriate as no parameter information is required.
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 specifies exactly what the tool returns: free keyless endpoints, bulk dataset info (with price if live), and license. It clearly distinguishes this manifest tool from sibling tools like get_stock or list_chains, which serve different purposes.
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?
No explicit guidance on when to use this tool versus alternatives. The description only states what it returns, not the context or prerequisites for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_stockGet one tokenized stockAInspect
Everything known about one stock: every instrument (one per issuer, with the mechanics union describing how it handles corporate actions), every listing under it (chain, contract address, supply, price), and the events recorded against it. Report instruments as the options; listings are addresses. Read mechanics.kind before reaching for any field inside mechanics.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | Ticker or slug, for example NVDA or nvda. |
Tool Definition Quality
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 includes a caution about reading `mechanics.kind` before other fields, which adds behavioral context. However, it does not disclose read-only nature, idempotency, or auth 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 is two well-structured sentences plus a key caution, all front-loaded. Every sentence adds useful information with no 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 complexity (multiple sub-components) and no output schema, the description covers the main return types (instruments, listings, events) and provides structural hints. It is complete for practical use, though a brief note on output format would elevate it.
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?
The input schema covers the single parameter `ticker` with 100% description coverage. The description adds value by clarifying that ticker can be a slug and provides an example ('NVDA or nvda').
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 it returns 'everything known about one stock' and enumerates the components: instruments, listings, events. It distinguishes between instruments and listings, providing precise vocabulary.
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 implies use when you need full details on a single stock, but does not explicitly state when to prefer this over siblings or when to avoid it. No alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_chainsList chainsAInspect
Every chain carrying tokenized stocks, with its explorer, and the matrix of which issuer has listed what where. Registry order, which is not a ranking.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It mentions completeness ('every chain') and ordering behavior, but does not disclose auth requirements or side effects. However, for a read-only list tool, this is minimally adequate.
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?
Single sentence with high information density, front-loaded with key details. Every word earns its place; no waste.
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?
For a zero-parameter tool with no output schema, the description is mostly complete. It explains what is listed and ordering, though slightly more behavioral context (e.g., read-only nature) would improve completeness.
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?
No parameters defined; baseline is 4. The description adds value by explaining what the output contains (chains, explorer, matrix) and ordering, exceeding the baseline.
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 it lists all chains carrying tokenized stocks, includes explorer and issuer listing matrix, and notes the order is registry order, not ranking. This distinguishes it from siblings like list_issuers and search_stocks.
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?
No explicit guidance on when to use or alternatives, but the purpose is clear enough that an agent can infer usage for getting chain data. No exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_eventsList corporate actionsAInspect
The corporate-action tape: splits, distributions and multiplier changes read from chain state, each with a transaction hash. Newest first by the time the event took effect, which is chronology and not a ranking.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many events to return. Default 50, maximum 500. | |
| since | No | ISO 8601 date. Only return events effective on or after this date. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that events are read from chain state and ordered chronologically (newest first). It does not mention side effects, error conditions, or rate limits, but for a read-only list operation it is fairly transparent.
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?
Two concise sentences front-load the core function and ordering, with no fluff. Every sentence adds value.
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?
No output schema, but description covers event types, transaction hash, and ordering. It does not detail exact fields returned or default limit, but schema provides that. Overall adequate for a list endpoint with simple parameters.
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 described). The description adds meaning by clarifying ordering semantics ('chronology, not ranking'), which helps interpret the 'since' parameter and overall output. This goes slightly beyond baseline.
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 it lists corporate actions (splits, distributions, multiplier changes) from chain state with transaction hashes. It is distinct from sibling tools like get_stock or list_issuers.
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 does not provide explicit guidance on when to use this tool versus alternatives. Usage is implied by describing what it does, but no 'when to use' or 'when not to use' is stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_issuersList issuersAInspect
Every issuer whose tokens are tracked, with the legal wrapper it uses, the chains it issues on, and its own statement on who may buy its tokens. The eligibility statement is reproduced verbatim and must be quoted, not summarised.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
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 explains the output content and gives a specific usage instruction: the eligibility statement must be quoted verbatim, not summarised. This adds valuable behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that conveys all necessary information without wasted words. It is front-loaded with the main purpose.
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 no input parameters or output schema, the description fully explains what the tool returns and provides usage guidance. It is complete for a list tool with these siblings.
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?
There are no parameters, so baseline is 4. The description adds value by detailing the output structure, which effectively describes the semantics of the tool.
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 it lists all tracked issuers with specific details (legal wrapper, chains, eligibility statement). This distinguishes it from sibling tools like list_chains or get_stock.
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 implies when to use: to get a comprehensive list of issuers. It does not explicitly state when not to use or provide alternatives, but the purpose is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_stocksSearch tokenized stocksAInspect
Find tokenized stocks by ticker or company name. Returns every match with its issuers and chains. If a ticker is a real stock but has not been tokenized, this reports that too, which is a correct answer rather than an empty result.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | A ticker (NVDA) or part of a company name (Nvidia). Case insensitive. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description discloses key behaviors: returns matches with issuers and chains, and handles untokenized stocks informatively. Adds context beyond schema.
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?
Two sentences, front-loaded with purpose, no wasted words. Efficiently communicates all 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 single parameter, no output schema, and no annotations, the description fully covers functionality, return content, and edge case behavior. Complete for agent use.
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 covers parameter with description; tool description adds case insensitivity and value types (ticker or part of name), enhancing semantic understanding.
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?
Description clearly states verb 'Find' and resource 'tokenized stocks', distinguishing from siblings like get_stock or list_chains by focusing on search by ticker or company name.
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 guidance on interpreting results: untokenized stocks reported as correct answers, not empty results. Implicitly suggests when to use but lacks explicit exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!